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Title: Greedy Multi-Step Off-Policy Reinforcement Learning. Abstract: This paper presents a novel multi-step reinforcement learning algorithms, named Greedy Multi-Step Value Iteration (GM-VI), under off-policy setting. GM-VI iteratively approximates the optimal value function of a given environment using a newly proposed multi-step bootstrapping technique, in which the step size is adaptively adjusted along each trajectory according to a greedy principle. With the improved multi-step information propagation mechanism, we show that the resulted VI process is capable of safely learning from arbitrary behavior policy without additional off-policy correction. We further analyze the theoretical properties of the corresponding operator, showing that it is able to converge to globally optimal value function, with a rate faster than traditional Bellman Optimality Operator. Experiments reveal that the proposed methods is reliable, easy to implement and achieves state-of-the-art performance on a series of standard benchmark datasets. | 2withdrawn
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Title: MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling. Abstract: Musical expression requires control of both what notes that are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience. | 1accept
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Title: Deep Q Learning from Dynamic Demonstration with Behavioral Cloning. Abstract: Although Deep Reinforcement Learning (DRL) has proven its capability to learn optimal policies by directly interacting with simulation environments, how to combine DRL with supervised learning and leverage additional knowledge to assist the DRL agent effectively still remains difficult. This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance. Specifically, the DQfDD-BC model leverages historical demonstrations to pre-train a supervised BC model and consistently update it by learning the dynamically updated demonstrations. Then the DQfDD-BC model manages the sample complexity by exploiting both the historical and generated demonstrations. An expert loss function is designed to compare actions generated by the DRL model with those obtained from the BC model to provide advantageous guidance for policy improvements. Experimental results in several OpenAI Gym environments show that the proposed approach adapts to different performance levels of demonstrations, and meanwhile, accelerates the learning processes. As illustrated in an ablation study, the dynamic demonstration and expert loss mechanisms with the utilization of a BC model contribute to improving the learning convergence performance compared with the origin DQfD model. | 0reject
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Title: Customizing Sequence Generation with Multi-Task Dynamical Systems. Abstract: Dynamical system models (including RNNs) often lack the ability to adapt the sequence generation or prediction to a given context, limiting their real-world application. In this paper we show that hierarchical multi-task dynamical systems (MTDSs) provide direct user control over sequence generation, via use of a latent code z that specifies the customization to the
individual data sequence. This enables style transfer, interpolation and morphing within generated sequences. We show the MTDS can improve predictions via latent code interpolation, and avoid the long-term performance degradation of standard RNN approaches. | 0reject
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Title: EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN. Abstract: Generative Adversarial Networks (GANs) are overwhelming in unsupervised learning tasks due to their expressive power in modeling fine-grained data distributions. However, it is challenging for GANs to model distributions of separate non-i.i.d. data partitions as it usually adopts an over-general prior, limiting its capability in capturing the latent structure of multiple data partitions and thus leading to mode collapse. In this paper, we present a new Bayesian GAN, dubbed expectation propagation prior GAN (EP-GAN), which addresses the above challenge of modeling non-i.i.d. federated data through imposing a partition-invariant prior distribution on a Bayesian GAN. Furthermore, unlike most existing algorithms for deep-learning-based EP inference that require numerical quadrature, here we propose a closed-form solution for each update step of EP, leading to a more efficient solution for federated data modeling. Experiments on both synthetic extremely non-i.i.d. image data partitions and realistic non-i.i.d. speech recognition tasks demonstrate that our framework effectively alleviates the performance deterioration caused by non-i.i.d. data. | 2withdrawn
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Title: Answer-based Adversarial Training for Generating Clarification Questions. Abstract: We propose a generative adversarial training approach for the problem of clarification question generation. Our approach generates clarification questions with the goal of eliciting new information that would make the given context more complete. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.
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Title: Unsupervised Word Translation Pairing using Refinement based Point Set Registration. Abstract: Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches rely on similarities in geometric structure of word embedding spaces across languages, to learn structure-preserving linear transformations using adversarial networks and refinement strategies. However, such techniques, in practice, tend to suffer from instability and convergence issues, requiring tedious fine-tuning for precise parameter setting. This paper proposes BioSpere, a novel framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space, by combining adversarial initialization and refinement procedure with point set registration algorithm used in image processing. We show that our framework alleviates the shortcomings of existing methodologies, and is relatively invariant to variable adversarial learning performance, depicting robustness in terms of parameter choices and training losses. Experimental evaluation on parallel dictionary induction task demonstrates state-of-the-art results for our framework on diverse language pairs. | 2withdrawn
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Title: Combining Global Sparse Gradients with Local Gradients. Abstract: Data-parallel neural network training is network-intensive, so gradient dropping was designed to exchange only large gradients. However, gradient dropping has been shown to slow convergence. We propose to improve convergence by having each node combine its locally computed gradient with the sparse global gradient exchanged over the network. We empirically confirm with machine translation tasks that gradient dropping with local gradients approaches convergence 48% faster than non-compressed multi-node training and 28% faster compared to vanilla gradient dropping. We also show that gradient dropping with a local gradient update does not reduce the model's final quality. | 2withdrawn
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Title: Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture. Abstract: This paper presents a new graph neural network architecture over which reinforcement learning can be performed to yield online policies for an important class of multi-robot task allocation (MRTA) problems, one that involves tasks with deadlines, and robots with ferry range and payload constraints and multi-tour capability. While drawing motivation from recent graph learning methods that learn to solve combinatorial optimization problems of the mTSP/VRP type, this paper seeks to provide better convergence and generalizability specifically for MRTA problems. The proposed neural architecture, called Covariant Attention-based Model or CAM, includes three main components: 1) an encoder: a covariant compositional node-based embedding is used to represent each task as a learnable feature vector in manner that preserves the local structure of the task graph while being invariant to the ordering of graph nodes; 2) context: a vector representation of the mission time and state of the concerned robot and its peers; and 2) a decoder: builds upon the attention mechanism to facilitate a sequential output. In order to train the CAM model, a policy-gradient method based on REINFORCE is used. While the new architecture can solve the broad class of MRTA problems stated above, to demonstrate real-world applicability we use a multi-unmanned aerial vehicle or multi-UAV-based flood response problem for evaluation purposes. For comparison, the well-known attention-based approach (designed to solve mTSP/VRP problems) is extended and applied to the MRTA problem, as a baseline. The results show that the proposed CAM method is not only superior to the baseline AM method in terms of the cost function (over training and unseen test scenarios), but also provide significantly faster convergence and yields learnt policies that can be executed within 2.4ms/robot, thereby allowing real-time application. | 0reject
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Title: Short optimization paths lead to good generalization. Abstract: Optimization and generalization are two essential aspects of machine learning. In this paper, we propose a framework to connect optimization with generalization by analyzing the generalization error based on the length of optimization trajectory under the gradient flow algorithm after convergence. Through our approach, we show that, with a proper initialization, gradient flow converges following a short path with an explicit length estimate. Such an estimate induces a length-based generalization bound, showing that short optimization paths after convergence indicate good generalization. Our framework can be applied to broad settings. For example, we use it to obtain generalization estimates on three distinct machine learning models: underdetermined $\ell_p$ linear regression, kernel regression, and overparameterized two-layer ReLU neural networks. | 0reject
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Title: More Side Information, Better Pruning: Shared-Label Classification as a Case Study. Abstract: Pruning of neural networks, also known as compression or sparsification, is the task of converting a given network, which may be too expensive to use (in prediction) on low resource platforms, with another 'lean' network which performs almost as well as the original one, while using considerably fewer resources. By turning the compression ratio knob, the practitioner can trade off the information gain versus the necessary computational resources, where information gain is a measure of reduction of uncertainty in the prediction.
In certain cases, however, the practitioner may readily possess some information on the prediction from other sources. The main question we study here is, whether it is possible to take advantage of the additional side information, in order to further reduce the computational resources, in tandem with the pruning process?
Motivated by a real-world application, we distill the following elegantly stated problem. We are given a multi-class prediction problem, combined with a (possibly pre-trained) network architecture for solving it on a given instance distribution, and also a method for pruning the network to allow trading off prediction speed with accuracy. We assume the network and the pruning methods are state-of-the-art, and it is not our goal here to improve them. However, instead of being asked to predict a single drawn instance $x$, we are being asked to predict the label of an $n$-tuple of instances $(x_1,\dots x_n)$, with the additional side information of all tuple instances share the same label. The shared label distribution is identical to the distribution on which the network was trained.
One trivial way to do this is by obtaining individual raw predictions for each of the $n$ instances (separately), using our given network, pruned for a desired accuracy, then taking the average to obtain a single more accurate prediction. This is simple to implement but intuitively sub-optimal, because the $n$ independent instantiations of the network do not share any information, and would probably waste resources on overlapping computation.
We propose various methods for performing this task, and compare them using extensive experiments on public benchmark data sets for image classification. Our comparison is based on measures of relative information (RI) and $n$-accuracy, which we define. Interestingly, we empirically find that I) sharing information between the $n$ independently computed hidden representations of $x_1,..,x_n$, using an LSTM based gadget, performs best, among all methods we experiment with, ii) for all methods studied, we exhibit a sweet spot phenomenon, which sheds light on the compression-information trade-off and may assist a practitioner to choose the desired compression ratio. | 0reject
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Title: Semantic Segmentation Based Unsupervised Domain Adaptation via Pseudo-Label Fusion. Abstract: In this paper, we propose a pseudo label fusion framework (PLF), a learning framework developed to deal with the domain gap between a source domain and a target domain for performing semantic segmentation based UDA in the unseen target domain. PLF fuses the pseudo labels generated by an ensemble of teacher models. The fused pseudo labels are then used by a student model to distill out the information embedded in these fused pseudo labels to perform semantic segmentation in the target domain. To examine the effectiveness of PLF, we perform a number of experiments on both GTA5 to Cityscapes and SYNTHIA to Cityscapes benchmarks to quantitatively and qualitatively inspect the improvements achieved by employing PLF in performing semantic segmentation in the target domain. Moreover, we provide a number of parameter analyses to validate that the choices made in the design of PLF is both practical and beneficial. Our experimental results on both benchmarks shows that PLF indeed offers adequate performance benefits in performing semantic segmentation in the unseen domain, and is able to achieve competitive performance when compared to the contemporary UDA techniques. | 2withdrawn
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Title: Poly-CAM: High resolution class activation map for convolutional neural networks. Abstract: The need for Explainable AI is increasing with the development of deep learning. The saliency maps derived from convolutional neural networks generally fail in localizing with accuracy the image features justifying the network prediction. This is because those maps are either low-resolution as for CAM (Zhou et al.,2016), or smooth as for perturbation-based methods (Zeiler & Fergus, 2014), or do correspond to a large number of widespread peaky spots as for gradient-based approaches (Sundararajan et al., 2017; Smilkov et al., 2017). In contrast, our work proposes to combine the information from earlier network layers with the one from later layers to produce a high resolution Class Activation Map that is competitive with the previous art in term of insertion-deletion faithfulness metrics, while out-performing it in term of precision of class-specific features localization. | 2withdrawn
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Title: Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning. Abstract: Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spirit of unsupervised learning in that the both seek to learn more effective and efficient learning procedure than learning from scratch. The fundamental difference of the two is that the most meta-learning approaches are supervised, assuming full access to the labels. However, acquiring labeled dataset for meta-training not only is costly as it requires human efforts in labeling but also limits its applications to pre-defined task distributions. In this paper, we propose a principled unsupervised meta-learning model, namely Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference. Moreover, we introduce a mixture of Gaussian (GMM) prior, assuming that each modality represents each class-concept in a randomly sampled episode, which we optimize with Expectation-Maximization (EM). Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors. We validate our model on Omniglot and Mini-ImageNet datasets by evaluating its performance on downstream few-shot classification tasks. The results show that our model obtain impressive performance gains over existing unsupervised meta-learning baselines, even outperforming supervised MAML on a certain setting. | 1accept
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Title: Cheap DNN Pruning with Performance Guarantees . Abstract: Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy. However most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. In this paper we propose a cheap pruning algorithm based on difference of convex (DC) optimisation. We also provide theoretical analysis for the growth in the Generalisation Error (GE) of the new pruned network. Our method can be used with any convex regulariser and allows for a controlled degradation in classification accuracy while being orders of magnitude faster than competing approaches. Experiments on common feedforward neural networks show that for sparsity levels above 90% our method achieves 10% higher classification accuracy compared to Hard Thresholding. | 0reject
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Title: Discovering the mechanics of hidden neurons. Abstract: Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding. This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons. While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown. We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing. During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training. During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction. These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments. | 0reject
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Title: Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch. Abstract: Semantic parsing which maps a natural language sentence into a formal machine-readable representation of its meaning, is highly constrained by the limited annotated training data. Inspired by the idea of coarse-to-fine, we propose a general-to-detailed neural network(GDNN) by incorporating cross-domain sketch(CDS) among utterances and their logic forms. For utterances in different domains, the General Network will extract CDS using an encoder-decoder model in a multi-task learning setup. Then for some utterances in a specific domain, the Detailed Network will generate the detailed target parts using sequence-to-sequence architecture with advanced attention to both utterance and generated CDS. Our experiments show that compared to direct multi-task learning, CDS has improved the performance in semantic parsing task which converts users' requests into meaning representation language(MRL). We also use experiments to illustrate that CDS works by adding some constraints to the target decoding process, which further proves the effectiveness and rationality of CDS. | 0reject
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Title: Reinforcement Learning with Probabilistically Complete Exploration. Abstract: Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in the sparse reward case, where they can do no better than to explore in all directions until the first positive rewards are found. To mitigate this, we propose Rapidly Randomly-exploring Reinforcement Learning (R3L). We formulate exploration as a search problem and leverage widely-used planning algorithms such as Rapidly-exploring Random Tree (RRT) to find initial solutions. These solutions are used as demonstrations to initialize a policy, then refined by a generic RL algorithm, leading to faster and more stable convergence. We provide theoretical guarantees of R3L exploration finding successful solutions, as well as bounds for its sampling complexity. We experimentally demonstrate the method outperforms classic and intrinsic exploration techniques, requiring only a fraction of exploration samples and achieving better asymptotic performance. | 0reject
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Title: Understanding the role of importance weighting for deep learning. Abstract: The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models. They observe that as long as the model can separate the training data, the impact of importance weighting diminishes as the training proceeds. Nevertheless, there lacks a rigorous characterization of this phenomenon. In this paper, we provide formal characterizations and theoretical justifications on the role of importance weighting with respect to the implicit bias of gradient descent and margin-based learning theory. We reveal both the optimization dynamics and generalization performance under deep learning models. Our work not only explains the various novel phenomenons observed for importance weighting in deep learning, but also extends to the studies where the weights are being optimized as part of the model, which applies to a number of topics under active research. | 1accept
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Title: Sparse-Complementary Convolution for Efficient Model Utilization on CNNs. Abstract: We introduce an efficient way to increase the accuracy of convolution neural networks (CNNs) based on high model utilization without increasing any computational complexity.
The proposed sparse-complementary convolution replaces regular convolution with sparse and complementary shapes of kernels, covering the same receptive field.
By the nature of deep learning, high model utilization of a CNN can be achieved with more simpler kernels rather than fewer complex kernels.
This simple but insightful model reuses of recent network architectures, ResNet and DenseNet, can provide better accuracy for most classification tasks (CIFAR-10/100 and ImageNet) compared to their baseline models. By simply replacing the convolution of a CNN with our sparse-complementary convolution, at the same FLOPs and parameters, we can improve top-1 accuracy on ImageNet by 0.33% and 0.18% for ResNet-101 and ResNet-152, respectively. A similar accuracy improvement could be gained by increasing the number of layers in those networks by ~1.5x. | 0reject
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Title: Complement Objective Training. Abstract: Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.
| 1accept
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Title: Molecular Graph Generation via Geometric Scattering. Abstract: Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. Both ligand and target molecules are represented as graphs with node and edge features encoding information about atomic elements and bonds respectively. Although existing deep learning models perform remarkably well at predicting physicochemical properties and binding affinities, the generation of new molecules with optimized properties remains challenging. Inherently, most GNNs perform poorly in whole-graph representation due to the limitations of the message-passing paradigm. Furthermore, step-by-step graph generation frameworks that use reinforcement learning or other sequential processing can be slow and result in a high proportion of invalid molecules with substantial post-processing needed in order to satisfy the principles of stoichiometry. To address these issues, we propose a representation-first approach to molecular graph generation. We guide the latent representation of an autoencoder by capturing graph structure information with the geometric scattering transform and apply penalties that structure the representation also by molecular properties. We show that this highly structured latent space can be directly used for molecular graph generation by the use of a GAN. We demonstrate that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis. | 2withdrawn
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Title: QGAN: Quantize Generative Adversarial Networks to Extreme low-bits. Abstract: The intensive computation and memory requirements of generative adversarial neural networks (GANs) hinder its real-world deployment on edge devices such as smartphones. Despite the success in model reduction of convolutional neural networks (CNNs), neural network quantization methods have not yet been studied on GANs, which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models. In this paper, we start with an extensive study on applying existing successful CNN quantization methods to quantize GAN models to extreme low bits. Our observation reveals that none of them generates samples with reasonable quality because of the underrepresentation of quantized weights in models, and the generator and discriminator networks show different sensitivities upon the quantization precision. Motivated by these observations, we develop a novel quantization method for GANs based on EM algorithms, named as QGAN. We also propose a multi-precision algorithm to help find an appropriate quantization precision of GANs given image qualities requirements. Experiments on CIFAR-10 and CelebA show that QGAN can quantize weights in GANs to even 1-bit or 2-bit representations with results of quality comparable to original models. | 0reject
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Title: Geometry aware convolutional filters for omnidirectional images representation. Abstract: Due to their wide field of view, omnidirectional cameras are frequently used by autonomous vehicles, drones and robots for navigation and other computer vision tasks. The images captured by such cameras, are often analysed and classified with techniques designed for planar images that unfortunately fail to properly handle the native geometry of such images. That results in suboptimal performance, and lack of truly meaningful visual features. In this paper we aim at improving popular deep convolutional neural networks so that they can properly take into account the specific properties of omnidirectional data. In particular we propose an algorithm that adapts convolutional layers, which often serve as a core building block of a CNN, to the properties of omnidirectional images. Thus, our filters have a shape and size that adapts with the location on the omnidirectional image. We show that our method is not limited to spherical surfaces and is able to incorporate the knowledge about any kind of omnidirectional geometry inside the deep learning network. As depicted by our experiments, our method outperforms the existing deep neural network techniques for omnidirectional image classification and compression tasks. | 0reject
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Title: WordsWorth Scores for Attacking CNNs and LSTMs for Text Classification. Abstract: Black box attacks on traditional deep learning models trained for text classifica- tion target important words in a piece of text, in order to change model prediction. Current approaches towards highlighting important features are time consuming and require large number of model queries. We present a simple yet novel method to calculate word importance scores, based on model predictions on single words. These scores, which we call WordsWorth scores, need to be calculated only once for the training vocabulary. They can be used to speed up any attack method that requires word importance, with negligible loss of attack performance. We run ex- periments on a number of datasets trained on word-level CNNs and LSTMs, for sentiment analysis and topic classification and compare to state-of-the-art base- lines. Our results show the effectiveness of our method in attacking these models with success rates that are close to the original baselines. We argue that global importance scores act as a very good proxy for word importance in a local context because words are a highly informative form of data. This aligns with the manner in which humans interpret language, with individual words having well- defined meaning and powerful connotations. We further show that these scores can be used as a debugging tool to interpret a trained model by highlighting rele- vant words for each class. Additionally, we demonstrate the effect of overtraining on word importance, compare the robustness of CNNs and LSTMs, and explain the transferability of adversarial examples across a CNN and an LSTM using these scores. We highlight the fact that neural networks make highly informative pre- dictions on single words. | 0reject
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Title: Pretrained models are active learners. Abstract: An important barrier to the safe deployment of machine learning systems is the risk of \emph{task ambiguity}, where multiple behaviors are consistent with the provided examples. We investigate whether pretrained models are better active learners, capable of asking for example labels that \textit{disambiguate} between the possible tasks a user may be trying to specify. Across a range of image and text datasets with spurious correlations, latent minority groups, or domain shifts, finetuning pretrained models with data acquired through simple uncertainty sampling achieves the same accuracy with \textbf{up to 6$\times$ fewer labels} compared to random sampling. Moreover, the examples chosen by these models are preferentially minority classes or informative examples where the spurious feature and class label are decorrelated. Notably, gains from active learning are not seen in unpretrained models, which do not select such examples, suggesting that the ability to actively learn is an emergent property of the pretraining process. | 0reject
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Title: Gaussian Process Neurons. Abstract: We propose a method to learn stochastic activation functions for use in probabilistic neural networks.
First, we develop a framework to embed stochastic activation functions based on Gaussian processes in probabilistic neural networks.
Second, we analytically derive expressions for the propagation of means and covariances in such a network, thus allowing for an efficient implementation and training without the need for sampling.
Third, we show how to apply variational Bayesian inference to regularize and efficiently train this model.
The resulting model can deal with uncertain inputs and implicitly provides an estimate of the confidence of its predictions.
Like a conventional neural network it can scale to datasets of arbitrary size and be extended with convolutional and recurrent connections, if desired. | 0reject
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Title: Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets. Abstract: Deep neural nets (DNNs) compression is crucial for adaptation to mobile devices. Though many successful algorithms exist to compress naturally trained DNNs, developing efficient and stable compression algorithms for robustly trained DNNs remains widely open. In this paper, we focus on a co-design of efficient DNN compression algorithms and sparse neural architectures for robust and accurate deep learning. Such a co-design enables us to advance the goal of accommodating both sparsity and robustness. With this objective in mind, we leverage the relaxed augmented Lagrangian based algorithms to prune the weights of adversarially trained DNNs, at both structured and unstructured levels. Using a Feynman-Kac formalism principled robust and sparse DNNs, we can at least double the channel sparsity of the adversarially trained ResNet20 for CIFAR10 classification, meanwhile, improve the natural accuracy by 8.69\% and the robust accuracy under the benchmark 20 iterations of IFGSM attack by 5.42\%. | 0reject
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Title: Explaining Knowledge Graph Embedding via Latent Rule Learning. Abstract: Knowledge Graph Embeddings (KGEs) embed entities and relations into continuous vector space following certain assumption, and are a powerful tools for representation learning of knowledge graphs. However, following vector space assumptions makes KGE a one step reasoner that directly predict final results without reasonable multi-hop reasoning steps. Thus KGEs are black-box models and explaining predictions made by KGEs remains unsolved. In this paper, we propose KGExplainer, the first general approach of providing explanations for predictions from KGE models. KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation. For explanation, KGExplainer outputs a ranked list of rules for each relation. Experiments on benchmark datasets with two target KGEs show that our approach is faithfulness to replicate KGEs behaviors for link prediction and is good at outputting quality rules for effective explanations. | 2withdrawn
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Title: Nonlinear Channels Aggregation Networks for Deep Action Recognition. Abstract: We introduce the concept of channel aggregation in ConvNet architecture, a novel compact representation of CNN features useful for explicitly modeling the nonlinear channels encoding especially when the new unit is embedded inside of deep architectures for action recognition. The channel aggregation is based on multiple-channels features of ConvNet and aims to be at the spot finding the optical convergence path at fast speed. We name our proposed convolutional architecture “nonlinear channels aggregation networks (NCAN)” and its new layer “nonlinear channels aggregation layer (NCAL)”. We theoretically motivate channels aggregation functions and empirically study their effect on convergence speed and classification accuracy. Another contribution in this work is an efficient and effective implementation of the NCAL, speeding it up orders of magnitude. We evaluate its performance on standard benchmarks UCF101 and HMDB51, and experimental results demonstrate that this formulation not only obtains a fast convergence but stronger generalization capability without sacrificing performance. | 2withdrawn
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Title: TRAKR – A reservoir-based tool for fast and accurate classification of neural time-series patterns. Abstract: Neuroscience has seen a dramatic increase in the types of recording modalities and complexity of neural time-series data collected from them. The brain is a highly recurrent system producing rich, complex dynamics that result in different behaviors. Correctly distinguishing such nonlinear neural time series in real-time, especially those with non-obvious links to behavior, could be useful for a wide variety of applications. These include detecting anomalous clinical events such as seizures in epilepsy, and identifying optimal control spaces for brain machine interfaces. It remains challenging to correctly distinguish nonlinear time-series patterns because of the high intrinsic dimensionality of such data, making accurate inference of state changes (for intervention or control) difficult. Simple distance metrics, which can be computed quickly do not yield accurate classifications. On the other end of the spectrum of classification methods, ensembles of classifiers or deep supervised tools offer higher accuracy but are slow, data-intensive, and computationally expensive. We introduce a reservoir-based tool, state tracker (TRAKR), which offers the high accuracy of ensembles or deep supervised methods while preserving the computational benefits of simple distance metrics. After one-shot training, TRAKR can accurately, and in real time, detect deviations in test patterns. By forcing the weighted dynamics of the reservoir to fit a desired pattern directly, we avoid many rounds of expensive optimization. Then, keeping the output weights frozen, we use the error signal generated by the reservoir in response to a particular test pattern as a classification boundary. We show that, using this approach, TRAKR accurately detects changes in synthetic time series. We then compare our tool to several others, showing that it achieves highest classification performance on a benchmark dataset–sequential MNIST–even when corrupted by noise. Additionally, we apply TRAKR to electrocorticography (ECoG) data from the macaque orbitofrontal cortex (OFC), a higher-order brain region involved in encoding the value of expected outcomes. We show that TRAKR can classify different behaviorally relevant epochs in the neural time series more accurately and efficiently than conventional approaches. Therefore, TRAKR can be used as a fast and accurate tool to distinguish patterns in complex nonlinear time-series data, such as neural recordings. | 0reject
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Title: Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension. Abstract: Robust subspace recovery (RSR) is the problem of learning a subspace from sample data points corrupted by outliers. Dual Principal Component Pursuit (DPCP) is a robust subspace recovery method that aims to find a basis for the orthogonal complement of the subspace by minimizing the sum of the distances of the points to the subspaces subject to orthogonality constraints on the basis. Prior work has shown that DPCP can provably recover the correct subspace in the presence of outliers as long as the true dimension of the subspace is known. In this paper, we show that if the orthogonality constraints --adopted in previous DPCP formulations-- are relaxed and random initialization is used instead of spectral one, DPCP can provably recover a subspace of \emph{unknown dimension}. Specifically, we propose a very simple algorithm based on running multiple instances of a projected sub-gradient descent method (PSGM), with each problem instance seeking to find one vector in the null space of the subspace. We theoretically prove that under mild conditions this approach succeeds with high probability. In particular, we show that 1) all of the problem instances will converge to a vector in the nullspace of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the nullspace of the subspace thus also revealing its true unknown codimension. We provide empirical results that corroborate our theoretical results and showcase the remarkable implicit rank regularization behavior of the PSGM algorithm that allows us to perform RSR without knowing the subspace dimension | 1accept
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Title: Maximum Entropy RL (Provably) Solves Some Robust RL Problems. Abstract: Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that maximum entropy (MaxEnt) RL maximizes a lower bound on a robust RL objective, and thus can be used to learn policies that are robust to some disturbances in the dynamics and the reward function. While this capability of MaxEnt RL has been observed empirically in prior work, to the best of our knowledge our work provides the first rigorous proof and theoretical characterization of the MaxEnt RL robust set. While a number of prior robust RL algorithms have been designed to handle similar disturbances to the reward function or dynamics, these methods typically require additional moving parts and hyperparameters on top of a base RL algorithm. In contrast, our results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications. While this does not imply that MaxEnt RL is the best available robust RL method, MaxEnt RL is a simple robust RL method with appealing formal guarantees. | 1accept
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Title: Adversarial Attacks on Copyright Detection Systems. Abstract: It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks. These vulnerabilities are especially apparent for neural network based systems. As proof of concept, we describe a well-known music identification method and implement this system in the form of a neural net. We then attack this system using simple gradient methods. Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system. Our goal is to raise awareness of the threats posed by adversarial examples in this space and to highlight the importance of hardening copyright detection systems to attacks. | 0reject
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Title: Reinforcement Learning with Random Delays. Abstract: Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the anatomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this principle to derive Delay-Correcting Actor-Critic (DCAC), an algorithm based on Soft Actor-Critic with significantly better performance in environments with delays. This is shown theoretically and also demonstrated practically on a delay-augmented version of the MuJoCo continuous control benchmark. | 1accept
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Title: Assessing and Developing Text-Based Agents for Morally Salient Scenarios. Abstract: When making everyday decisions, people are guided by their conscience, an internal sense of right and wrong, to behave morally. By contrast, artificial agents may behave immorally when trained on environments that ignore moral concerns, such as violent video games. With the advent of generally capable agents that pretrain on many environments, mitigating inherited biases towards immoral behavior will become necessary. However, prior work on aligning agents with human values and morals focuses on small-scale settings lacking in semantic complexity. To enable research in larger, more realistic settings, we introduce Jiminy Cricket, an environment suite of 25 text-based adventure games with thousands of semantically rich, morally salient scenarios. Via dense annotations for every possible action, Jiminy Cricket environments robustly evaluate whether agents can act morally while maximizing reward. To improve moral behavior, we leverage language models with commonsense moral knowledge and develop strategies to mediate this knowledge into actions. In extensive experiments, we find that our approach can steer agents towards moral behavior without sacrificing performance. | 2withdrawn
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Title: Neural Partial Differential Equations with Functional Convolution. Abstract: We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs. Our key idea is to leverage the prior of ``"translational similarity" of numerical PDE differential operators to drastically reduce the scale of learning model and training data. We implemented three central network components, including a neural functional convolution operator, a Picard forward iterative procedure, and an adjoint backward gradient calculator. Our novel paradigm fully leverages the multifaceted priors that stem from the sparse and smooth nature of the physical PDE solution manifold and the various mature numerical techniques such as adjoint solver, linearization, and iterative procedure to accelerate the computation. We demonstrate the efficacy of our method by robustly discovering the model and accurately predicting the solutions of various types of PDEs with small-scale networks and training sets. We highlight that all the PDE examples we showed were trained with up to 8 data samples and within 325 network parameters. | 0reject
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Title: De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning. Abstract: Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery. Current approaches to develop molecules for a given protein target are intuition-driven, hampered by slow iterative design-test cycles due to computational challenges in utilizing 3D structural data, and ultimately limited by the expertise of the chemist – leading to bottlenecks in molecular design. In this contribution, we propose a novel framework, called 3D-MolGNN_RL, coupling reinforcement learning (RL) to a deep generative model based on 3D-Scaffold to generate target candidates specific to a protein pocket building up atom by atom from the starting core scaffold. 3D-MolGNN_RL provides an efficient way to optimize key features by multi-objective reward function within a protein pocket using parallel graph neural network models. The agent learns to build molecules in 3D space while optimizing the binding affinity, potency, and synthetic accessibility of the candidates generated for the SARS-CoV-2 Main Protease. | 2withdrawn
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Title: On learning visual odometry errors. Abstract: This paper fosters the idea that deep learning methods can be sided to classical
visual odometry pipelines to improve their accuracy and to produce uncertainty
models to their estimations. We show that the biases inherent to the visual odom-
etry process can be faithfully learnt and compensated for, and that a learning ar-
chitecture associated to a probabilistic loss function can jointly estimate a full
covariance matrix of the residual errors, defining a heteroscedastic error model.
Experiments on autonomous driving image sequences and micro aerial vehicles
camera acquisitions assess the possibility to concurrently improve visual odome-
try and estimate an error associated to its outputs. | 2withdrawn
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Title: Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling. Abstract: We perform systematically and fairly controlled experiments with the 6-layer Transformer to investigate the hardness in conditional-language-modeling languages which have been traditionally considered morphologically rich (AR and RU) and poor (ZH). We evaluate through statistical comparisons across 30 possible language directions from the 6 languages of the United Nations Parallel Corpus across 5 data sizes on 3 representation levels --- character, byte, and word. Results show that performance is relative to the representation granularity of each of the languages, not to the language as a whole. On the character and byte levels, we are able to eliminate statistically significant performance disparity, hence demonstrating that a language cannot be intrinsically hard. The disparity that mirrors the morphological complexity hierarchy is shown to be a byproduct of word segmentation. Evidence from data statistics, along with the fact that word segmentation is qualitatively indeterminate, renders a decades-long debate on morphological complexity (unless it is being intentionally modeled in a word-based, meaning-driven context) irrelevant in the context of computing. The intent of our work is to help effect more objectivity and adequacy in evaluation as well as fairness and inclusivity in experimental setup in the area of language and computing so to uphold diversity in Machine Learning and Artificial Intelligence research. Multilinguality is real and relevant in computing not due to canonical, structural linguistic concepts such as morphology or "words" in our minds, but rather standards related to internationalization and localization, such as character encoding --- something which has thus far been sorely overlooked in our discourse and curricula. | 1accept
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Title: Robustness to Pruning Predicts Generalization in Deep Neural Networks. Abstract: Why over-parameterized neural networks generalize as well as they do is a central concern of theoretical analysis in machine learning today. Following Occam's razor, it has long been suggested that simpler networks generalize better than more complex ones. Successfully quantifying this principle has proved difficult given that many measures of simplicity, such as parameter norms, grow with the size of the network and thus fail to capture the observation that larger networks tend to generalize better in practice.
In this paper, we introduce a new, theoretically motivated measure of a network's simplicity: the smallest fraction of the network's parameters that can be kept while pruning without adversely affecting its training loss. We show that this measure is highly predictive of a model's generalization performance across a large set of convolutional networks trained on CIFAR-10. Lastly, we study the mutual information between the predictions of our new measure and strong existing measures based on models' margin, flatness of minima and optimization speed. We show that our new measure is similar to -- but more predictive than -- existing flatness-based measures. | 0reject
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Title: Stochastic Latent Residual Video Prediction. Abstract: Video prediction is a challenging task: models have to account for the inherent uncertainty of the future. Most works in the literature are based on stochastic image-autoregressive recurrent networks, raising several performance and applicability issues. An alternative is to use fully latent temporal models which untie frame synthesis and dynamics. However, no such model for video prediction has been proposed in the literature yet, due to design and training difficulties. In this paper, we overcome these difficulties by introducing a novel stochastic temporal model. It is based on residual updates of a latent state, motivated by discretization schemes of differential equations. This first-order principle naturally models video dynamics as it allows our simpler, lightweight, interpretable, latent model to outperform prior state-of-the-art methods on challenging datasets. | 0reject
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Title: Projective Subspace Networks For Few-Shot Learning. Abstract: Generalization from limited examples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of lifelong learning. In this paper, we introduce the Projective Subspace Networks (PSN), a deep learning paradigm that learns non-linear embeddings from limited supervision. In contrast to previous studies, the embedding in PSN deems samples of a given class to form an affine subspace. We will show that such modeling leads to robust solutions, yielding competitive results on supervised and semi-supervised few-shot classification. Moreover, our PSN approach has the ability of end-to-end learning. In contrast to previous works, our projective subspace can be thought of as a richer representation capturing higher-order information datapoints for modeling new concepts. | 0reject
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Title: Learning Video Representations using Contrastive Bidirectional Transformer. Abstract: This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods. Our method extends the BERT model for text sequences to the case of sequences of real-valued feature vectors, by replacing the softmax loss with noise contrastive estimation (NCE). We also show how to learn representations from sequences of visual features and sequences of words derived from ASR (automatic speech recognition), and show that such cross-modal training (when possible) helps even more. | 0reject
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Title: Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. Abstract: Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score. | 1accept
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Title: A generalized probability kernel on discrete distributions and its application in two-sample test. Abstract: We propose a generalized probability kernel(GPK) on discrete distributions with finite support. This probability kernel, defined as kernel between distributions instead of samples, generalizes the existing discrepancy statistics such as maximum mean discrepancy(MMD) as well as probability product kernels, and extends to more general cases. For both existing and newly proposed statistics, we estimate them through empirical frequency and illustrate the strategy to analyze the resulting bias and convergence bounds. We further propose power-MMD, a natural extension of MMD in the framework of GPK, illustrating its usage for the task of two-sample test. Our work connects the fields of discrete distribution-property estimation and kernel-based hypothesis test, which might shed light on more new possibilities. | 0reject
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Title: Disentangled cyclic reconstruction for domain adaptation. Abstract: The domain adaptation problem involves learning a unique classification or regression model capable of performing on both a source and a target domain. Although the labels for the source data are available during training, the labels in the target domain are unknown. An effective way to tackle this problem lies in extracting insightful features invariant to the source and target domains. In this work, we propose splitting the information for each domain into a task-related representation and its complimentary context representation. We propose an original method to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation problem. In particular, our method allows disentanglement in the target domain, despite the absence of training labels. This enables the isolation of task-specific information from both domains and a projection into a common representation. The task-specific representation allows efficient transfer of knowledge acquired from the source domain to the target domain. We validate the proposed method on several classical domain adaptation benchmarks and illustrate the benefits of disentanglement for domain adaptation. | 0reject
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Title: Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos. Abstract: The recent framework of performative prediction is aimed at capturing settings where predictions influence the target/outcome they try to predict. In this paper, we introduce a natural multi-agent version of this framework, where multiple decision makers try to predict the same outcome. We showcase that such competition can result in interesting phenomena by proving the possibility of phase transitions from stability to instability and eventually chaos. Specifically, we present settings of multi-agent performative prediction where under sufficient conditions, their dynamics lead to global stability and optimality. In the opposite direction, when the agents are not sufficiently cautious in their learning/updates rates, we show that instability and in fact formal chaos is possible. We complement our theoretical predictions with simulations showcasing the predictive power of our results. | 0reject
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Title: Isometric Transformation Invariant and Equivariant Graph Convolutional Networks. Abstract: Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as physical simulations. A crucial requirement for applying graphs in Euclidean spaces to physical simulations is learning and inferring the isometric transformation invariant and equivariant features in a computationally efficient manner. In this paper, we propose a set of transformation invariant and equivariant models based on graph convolutional networks, called IsoGCNs. We demonstrate that the proposed model has a competitive performance compared to state-of-the-art methods on tasks related to geometrical and physical simulation data. Moreover, the proposed model can scale up to graphs with 1M vertices and conduct an inference faster than a conventional finite element analysis, which the existing equivariant models cannot achieve. | 1accept
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Title: Few-shot Learning via Dirichlet Tessellation Ensemble. Abstract: Few-shot learning (FSL) is the process of rapid generalization from abundant base samples to inadequate novel samples. Despite extensive research in recent years, FSL is still not yet able to generate satisfactory solutions for a wide range of real-world applications. To confront this challenge, we study the FSL problem from a geometric point of view in this paper. One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space. We retrofit it by making use of a recent advance in computational geometry called Cluster-induced Voronoi Diagram (CIVD). Starting from the simplest nearest neighbor model, CIVD gradually incorporates cluster-to-point and then cluster-to-cluster relationships for space subdivision, which is used to improve the accuracy and robustness at multiple stages of FSL. Specifically, we use CIVD (1) to integrate parametric and nonparametric few-shot classifiers; (2) to combine feature representation and surrogate representation; (3) and to leverage feature-level, transformation-level, and geometry-level heterogeneities for a better ensemble. Our CIVD-based workflow enables us to achieve new state-of-the-art results on mini-ImageNet, CUB, and tiered-ImagenNet datasets, with ${\sim}2\%{-}5\%$ improvements upon the next best. To summarize, CIVD provides a mathematically elegant and geometrically interpretable framework that compensates for extreme data insufficiency, prevents overfitting, and allows for fast geometric ensemble for thousands of individual VD. These together make FSL stronger. | 1accept
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Title: NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension. Abstract: Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them. In this process, it is essential to properly determine whether an answer to the question exists in a given document. This task often becomes complicated when the question involves multiple different conditions or requirements which are to be met in the answer. For example, in a question "What was the projection of sea level increases in the fourth assessment report?", the answer should properly satisfy several conditions, such as "increases" (but not decreases) and "fourth" (but not third). To address this, we propose a neural question requirement inspection model called NeurQuRI that extracts a list of conditions from the question, each of which should be satisfied by the candidate answer generated by an MRC model. To check whether each condition is met, we propose a novel, attention-based loss function. We evaluate our approach on SQuAD 2.0 dataset by integrating the proposed module with various MRC models, demonstrating the consistent performance improvements across a wide range of state-of-the-art methods. | 1accept
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Title: Training Provably Robust Models by Polyhedral Envelope Regularization. Abstract: Training certifiable neural networks enables one to obtain models with robustness guarantees against adversarial attacks. In this work, we use a linear approximation to bound model’s output given an input adversarial budget. This allows us to bound the adversary-free region in the data neighborhood by a polyhedral envelope and yields finer-grained certified robustness than existing methods. We further exploit this certifier to introduce a framework called polyhedral envelope regular- ization (PER), which encourages larger polyhedral envelopes and thus improves the provable robustness of the models. We demonstrate the flexibility and effectiveness of our framework on standard benchmarks; it applies to networks with general activation functions and obtains comparable or better robustness guarantees than state-of-the-art methods, with very little cost in clean accuracy, i.e., without over-regularizing the model. | 0reject
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Title: Learning Synthetic Environments and Reward Networks for Reinforcement Learning. Abstract: We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by learning about its state dynamics and rewards, an RN is a partial proxy that learns to augment or replace rewards. We use bi-level optimization to evolve SEs and RNs: the inner loop trains the RL agent, and the outer loop trains the parameters of the SE / RN via an evolution strategy. We evaluate our proposed new concept on a broad range of RL algorithms and classic control environments. In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment. However, once such an SE has been learned, we do not need any interactions with the real environment to train new agents. Moreover, the learned SE proxies allow us to train agents with fewer interactions while maintaining the original task performance. Our empirical results suggest that SEs achieve this result by learning informed representations that bias the agents towards relevant states. Moreover, we find that these proxies are robust against hyperparameter variation and can also transfer to unseen agents. | 1accept
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Title: Causal Reasoning from Meta-reinforcement learning. Abstract: Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning. We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data. Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions. Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches. More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments. | 0reject
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Title: A Synaptic Neural Network and Synapse Learning. Abstract: A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear synapse function of excitatory and inhibitory channel probabilities. Introduced the concept of surprisal space and constructed a commutative diagram, we proved that the inhibitory probability function -log(1-exp(-x)) in surprisal space is the topologically conjugate function of the inhibitory complementary probability 1-x in probability space. Furthermore, we found that the derivative of the synapse over the parameter in the surprisal space is equal to the negative Bose-Einstein distribution. In addition, we constructed a fully connected synapse graph (tensor) as a synapse block of a synaptic neural network. Moreover, we proved the gradient formula of a cross-entropy loss function over parameters, so synapse learning can work with the gradient descent and backpropagation algorithms. In the proof-of-concept experiment, we performed an MNIST training and testing on the MLP model with synapse network as hidden layers. | 0reject
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Title: Calibration of Neural Networks using Splines. Abstract: Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities. Measuring calibration error amounts to comparing two empirical distributions. In this work, we introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test in which the main idea is to compare the respective cumulative probability distributions. From this, by approximating the empirical cumulative distribution using a differentiable function via splines, we obtain a recalibration function, which maps the network outputs to actual (calibrated) class assignment probabilities. The spline-fitting is performed using a held-out calibration set and the obtained recalibration function is evaluated on an unseen test set. We tested our method against existing calibration approaches on various image classification datasets and our spline-based recalibration approach consistently outperforms existing methods on KS error as well as other commonly used calibration measures. Code is available online at https://github.com/kartikgupta-at-anu/spline-calibration. | 1accept
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Title: Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling. Abstract: Although large-scale pre-trained neural models have shown impressive performances in a variety of tasks, their ability to generate coherent text that appropriately models discourse phenomena is harder to evaluate and less understood. Given the claims of improved text generation quality across various systems, we consider the coherence evaluation of machine generated text to be one of the principal applications of coherence models that needs to be investigated. We explore training data and self-supervision objectives that result in a model that generalizes well across tasks and can be used off-the-shelf to perform such evaluations.
Prior work in neural coherence modeling has primarily focused on devising new architectures, and trained the model to distinguish coherent and incoherent text through pairwise self-supervision on the permuted documents task. We instead use a basic model architecture and show significant improvements over state of the art within the same training regime. We then design a harder self-supervision objective by increasing the ratio of negative samples within a contrastive learning setup, and enhance the model further through automatic hard negative mining coupled with a large global negative queue encoded by a momentum encoder. We show empirically that increasing the density of negative samples improves the basic model, and using a global negative queue further improves and stabilizes the model while training with hard negative samples. We evaluate the coherence model on task-independent test sets that resemble real-world use cases and show significant improvements in coherence evaluations of downstream applications. | 2withdrawn
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Title: Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy. Abstract: Somatic cancer mutation detection at ultra-low variant allele frequencies (VAFs) is an unmet challenge that is intractable with current state-of-the-art mutation calling methods. Specifically, the limit of VAF detection is closely related to the depth of coverage, due to the requirement of multiple supporting reads in extant methods, precluding the detection of mutations at VAFs that are orders of magnitude lower than the depth of coverage. Nevertheless, the ability to detect cancer-associated mutations in ultra low VAFs is a fundamental requirement for low-tumor burden cancer diagnostics applications such as early detection, monitoring, and therapy nomination using liquid biopsy methods (cell-free DNA). Here we defined a spatial representation of sequencing information adapted for convolutional architecture that enables variant detection at VAFs, in a manner independent of the depth of sequencing. This method enables the detection of cancer mutations even in VAFs as low as 10x-4^, >2 orders of magnitude below the current state-of-the-art. We validated our method on both simulated plasma and on clinical cfDNA plasma samples from cancer patients and non-cancer controls. This method introduces a new domain within bioinformatics and personalized medicine – somatic whole genome mutation calling for liquid biopsy. | 0reject
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Title: Phase Transitions for the Information Bottleneck in Representation Learning. Abstract: In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation? In this paper, we set out to answer these questions by studying multiple phase transitions in the IB objective: IB_β[p(z|x)] = I(X; Z) − βI(Y; Z) defined on the encoding distribution p(z|x) for input X, target Y and representation Z, where sudden jumps of dI(Y; Z)/dβ and prediction accuracy are observed with increasing β. We introduce a definition for IB phase transitions as a qualitative change of the IB loss landscape, and show that the transitions correspond to the onset of learning new classes. Using second-order calculus of variations, we derive a formula that provides a practical condition for IB phase transitions, and draw its connection with the Fisher information matrix for parameterized models. We provide two perspectives to understand the formula, revealing that each IB phase transition is finding a component of maximum (nonlinear) correlation between X and Y orthogonal to the learned representation, in close analogy with canonical-correlation analysis (CCA) in linear settings. Based on the theory, we present an algorithm for discovering phase transition points. Finally, we verify that our theory and algorithm accurately predict phase transitions in categorical datasets, predict the onset of learning new classes and class difficulty in MNIST, and predict prominent phase transitions in CIFAR10.
| 1accept
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Title: NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. Abstract: Most existing neural architecture search (NAS) benchmarks and algorithms prioritize performance on well-studied tasks, e.g., image classification on CIFAR and ImageNet. This makes the applicability of NAS approaches in more diverse areas inadequately understood.
In this paper, we present NAS-Bench-360, a benchmark suite for evaluating state-of-the-art NAS methods for convolutional neural networks (CNNs). To construct it, we curate a collection of ten tasks spanning a diverse array of application domains, dataset sizes, problem dimensionalities, and learning objectives. By carefully selecting tasks that can both interoperate with modern CNN-based search methods but that are also far-afield from their original development domain, we can use NAS-Bench-360 to investigate the following central question: do existing state-of-the-art NAS methods perform well on diverse tasks? Our experiments show that a modern NAS procedure designed for image classification can indeed find good architectures for tasks with other dimensionalities and learning objectives; however, the same method struggles against more task-specific methods and performs catastrophically poorly on classification in non-vision domains. The case for NAS robustness becomes even more dire in a resource-constrained setting, where a recent NAS method provides little-to-no benefit over much simpler baselines. These results demonstrate the need for a benchmark such as NAS-Bench-360 to help develop NAS approaches that work well on a variety of tasks, a crucial component of a truly robust and automated pipeline. We conclude with a demonstration of the kind of future research our suite of tasks will enable. All data and code is made publicly available. | 0reject
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Title: Single-Node Attack for Fooling Graph Neural Networks. Abstract: Graph neural networks (GNNs) have shown broad applicability in a variety of domains.
Some of these domains, such as social networks and product recommendations, are fertile ground for malicious users and behavior.
In this paper, we show that GNNs are vulnerable to the extremely limited scenario of a single-node adversarial example, where the node cannot be picked by the attacker.
That is, an attacker can force the GNN to classify any target node to a chosen label by only slightly perturbing another single arbitrary node in the graph, even when not being able to pick that specific attacker node. When the adversary is allowed to pick a specific attacker node, the attack is even more effective.
We show that this attack is effective across various GNN types (e.g., GraphSAGE, GCN, GAT, and GIN), across a variety of real-world datasets, and as a targeted and non-targeted attack.
Our code is available anonymously at https://github.com/gnnattack/SINGLE . | 0reject
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Title: Meta Adversarial Training. Abstract: Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomous driving. These attacks place adversarial artifacts in the physical world that indirectly cause the addition of universal perturbations to inputs of a model that can fool it in a variety of contexts. Adversarial training is the most effective defense against image-dependent adversarial attacks. However, tailoring adversarial training to universal perturbations is computationally expensive since the optimal universal perturbations depend on the model weights which change during training. We propose meta adversarial training (MAT), a novel combination of adversarial training with meta-learning, which overcomes this challenge by meta-learning universal perturbations along with model training. MAT requires little extra computation while continuously adapting a large set of perturbations to the current model. We present results for universal patch and universal perturbation attacks on image classification and traffic-light detection. MAT considerably increases robustness against universal patch attacks compared to prior work. | 0reject
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Title: Lung Tumor Location and Identification with AlexNet and a Custom CNN. Abstract: Lung cancer is the leading cause of cancer deaths in the world and early detection is a crucial part of increasing patient survival. Deep learning techniques provide us with a method of automated analysis of patient scans. In this work, we compare AlexNet, a multi-layered and highly flexible architecture, with a custom CNN to determine if lung nodules with patient scans are benign or cancerous. We have found our CNN architecture to be highly accurate (99.79%) and fast while maintaining low False Positive and False Negative rates (< 0.01% and 0.15% respectively). This is important as high false positive rates are a serious issue with lung cancer diagnosis. We have found that AlexNet is not well suited to the problem of nodule identification, though it is a good baseline comparison because of its flexibility. | 0reject
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Title: LocalGAN: Modeling Local Distributions for Adversarial Response Generation. Abstract: This paper presents a new methodology for modeling the local semantic distribution of responses to a given query in the human-conversation corpus, and on this basis, explores a specified adversarial learning mechanism for training Neural Response Generation (NRG) models to build conversational agents. The proposed mechanism aims to address the training instability problem and improve the quality of generated results of Generative Adversarial Nets (GAN) in their utilizations in the response generation scenario. Our investigation begins with the thorough discussions upon the objective function brought by general GAN architectures to NRG models, and the training instability problem is proved to be ascribed to the special local distributions of conversational corpora. Consequently, an energy function is employed to estimate the status of a local area restricted by the query and its responses in the semantic space, and the mathematical approximation of this energy-based distribution is finally found. Building on this foundation, a local distribution oriented objective is proposed and combined with the original objective, working as a hybrid loss for the adversarial training of response generation models, named as LocalGAN. Our experimental results demonstrate that the reasonable local distribution modeling of the query-response corpus is of great importance to adversarial NRG, and our proposed LocalGAN is promising for improving both the training stability and the quality of generated results.
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Title: Learning to Explore with Pleasure. Abstract: Exploration is a long-standing challenge in sequential decision problem in machine learning. This paper investigates the adoption of two theories of optimal stimulation level - "the pacer principle" and the Wundt curve - from psychology to improve the exploration challenges. We propose a method called exploration with pleasure (EP) which is formulated based on the notion of pleasure as defined in accordance with the above two theories. EP is able to identify the region of stimulations that will trigger pleasure to the learning agent during exploration and consequently improve on the learning process. The effectiveness of EP is studied in two machine learning settings: curiosity-driven reinforcement learning (RL) and Bayesian optimisation (BO). Experiments in purely curiosity-driven RL show that by using EP to generate intrinsic rewards, it can yield faster learning. Experiments in BO demonstrate that by using EP to specify the exploration parameters in two acquisition functions - Probability of Improvement and Expected Improvement - it can achieve faster convergence and better function values.
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Title: Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?. Abstract: Bayesian learning via Stochastic Gradient Langevin Dynamics (SGLD) has been suggested for differentially private learning. While previous research provides differential privacy bounds for SGLD when close to convergence or at the initial steps of the algorithm, the question of what differential privacy guarantees can be made in between remains unanswered. This interim region is essential, especially for Bayesian neural networks, as it is hard to guarantee convergence to the posterior. This paper will show that using SGLD might result in unbounded privacy loss for this interim region, even when sampling from the posterior is as differentially private as desired. | 0reject
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Title: Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks. Abstract: Different nodes in a graph neighborhood generally yield different importance. In previous work of Graph Convolutional Networks (GCNs), such differences are typically modeled with attention mechanisms. However, as we prove in our paper, soft attention weights suffer from over-smoothness in large neighborhoods. To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs). ARFs enable GCNs to get rid of the over-smoothness of soft attention weights, as well as to efficiently explore long-distance dependencies in graphs. We further propose GRARF (GCN with Reinforced Adaptive Receptive Fields) as an instance, where an optimal policy of constructing ARFs is learned with reinforcement learning. GRARF achieves or matches state-of-the-art performances on public datasets from different domains. Our further analysis corroborates that GRARF is more robust than attention models against neighborhood noises. | 0reject
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Title: SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. Abstract: Many deep learning applications benefit from using large models with billions of parameters. These models can only be trained with specialized distributed training algorithms that require low-latency and high-bandwidth interconnect. As a result, large models are typically trained in dedicated GPU clusters that can be extremely costly to deploy and operate. In contrast, there are more affordable distributed training setups, such as using cheap "preemptible" instances or pooling together existing resources from multiple regions. However, both these setups come with unique challenges that make it impractical to train large models using conventional model parallelism. In this work, we carefully analyze these challenges and find configurations where training larger models becomes less communication-intensive. Based on these observations, we propose SWARM Parallelism (Stochastically Wired Adaptively Rebalanced Model Parallelism) — a model-parallel training algorithm designed for swarms of poorly connected, heterogeneous unreliable devices. SWARM creates temporary randomized pipelines between available nodes that are rebalanced in case of failure. To further reduce the network usage of our approach, we develop several compression-aware architecture modifications and evaluate their tradeoffs. Finally, we combine our insights to train a large Transformer language model with 1.1B shared parameters (approximately 13B before sharing) on a swarm of preemptible T4 GPUs with less than 400Mb/s network throughput. | 0reject
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Title: Unbiased Learning with State-Conditioned Rewards in Adversarial Imitation Learning. Abstract: Adversarial imitation learning has emerged as a general and scalable framework for automatic reward acquisition. However, we point out that previous methods commonly exploited occupancy-dependent reward learning formulation—which hinders the reconstruction of optimal decision as an energy-based model. Despite the theoretical justification, the occupancy measures tend to cause issues in practice because of high variance and low vulnerability to domain shifts. Another reported problem is termination biases induced by provided rewarding and regularization schemes around terminal states. In order to deal with these issues, this work presents a novel algorithm called causal adversarial inverse reinforcement learning. Our formulation draws a strong connection between adversarial learning and energy-based reinforcement learning; thus, the architecture is capable of recovering a reward function that induces a multi-modal policy. In experiments, we demonstrate that our approach outperforms prior methods in challenging continuous control tasks, even under significant variation in the environments. | 0reject
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Title: Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations. Abstract: Owing much to the revolution of information technology, recent progress of deep learning benefits incredibly from the vastly enhanced access to data available in various digital formats. Yet those publicly accessible information also raises a fundamental issue concerning Intellectual Property, that is, how to precisely control legal or illegal exploitation of a dataset for training commercial models. To tackle this issue, this paper introduces and investigates a new concept called ''learnability lock'' for securing the process of data authorization. In particular, we propose adversarial invertible transformation, that can be viewed as a mapping from image to image, to encrypt data samples so that they become ''unlearnable'' by machine learning models with negligible loss of visual features. Meanwhile, authorized clients can use a specific key to unlock the learnability of the protected dataset and train models normally. The proposed learnability lock leverages class-wise perturbation that applies a universal transformation function on data samples of the same label. This ensures that the learnability can be easily restored with a simple inverse transformation while remaining difficult to be detected or reverse-engineered. We empirically demonstrate the success and practicability of our method on visual classification tasks. | 1accept
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Title: Massively Parallel Hyperparameter Tuning. Abstract: Modern learning models are characterized by large hyperparameter spaces. In order to adequately explore these large spaces, we must evaluate a large number of configurations, typically orders of magnitude more configurations than available parallel workers. Given the growing costs of model training, we would ideally like to perform this search in roughly the same wall-clock time needed to train a single model. In this work, we tackle this challenge by introducing ASHA, a simple and robust hyperparameter tuning algorithm with solid theoretical underpinnings that exploits parallelism and aggressive early-stopping. Our extensive empirical results show that ASHA outperforms state-of-the-art hyperparameter tuning methods; scales linearly with the number of workers in distributed settings; converges to a high quality configuration in half the time taken by Vizier, Google's internal hyperparameter tuning service) in an experiment with 500 workers; and beats the published result for a near state-of-the-art LSTM architecture in under $2\times$ the time to train a single model. | 0reject
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Title: Lottery Tickets can have Structural Sparsity. Abstract: The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse subnetworks (i.e., $\textit{winning tickets}$) that can be trained in isolation to match full accuracy. Despite many exciting efforts being made, there is one "commonsense" seldomly challenged: a winning ticket is found by iterative magnitude pruning (IMP) and hence the resultant pruned subnetworks have only unstructured sparsity. That gap limits the appeal of winning tickets in practice, since the highly irregular sparse patterns are challenging to accelerate on hardware. Meanwhile, directly substituting structured pruning for unstructured pruning in IMP damages performance more severely and is usually unable to locate winning tickets.
In this paper, we demonstrate $\textbf{the first positive result}$ that a structurally sparse winning ticket can be effectively found in general. The core idea is to append ``post-processing techniques" after each round of (unstructured) IMP, to enforce the formation of structural sparsity. Specifically, we first ``re-fill" pruned elements back in some channels deemed to be important, and then ``re-group" non-zero elements to create flexible group-wise structural patterns. Both our identified channel- and group-wise structural subnetworks win the lottery, with substantial inference speedups readily supported by practical hardware. Extensive experiments, conducted on diverse datasets across multiple network backbones, consistently validate our proposal, showing that the hardware acceleration roadblock of LTH is now removed. Specifically, the structural winning tickets obtain up to $\{64.93\%, 64.84\%, 64.84\%\}$ running time savings at $\{36\%\sim 80\%, 74\%, 58\%\}$ sparsity on CIFAR, Tiny-ImageNet, ImageNet, while maintaining comparable accuracy. All the codes and pre-trained models will be publicly released. | 0reject
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Title: DEGREE: Decomposition Based Explanation for Graph Neural Networks. Abstract: Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximation based and perturbation based approaches with suffer from faithfulness problems and unnatural artifacts respectively. To tackle these problems, we propose DEGREE (Decomposition based Explanation for GRaph nEural nEtworks) to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. Based on this, we further design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods. The efficiency of our algorithm can be further improved by utilizing GNN characteristics. Finally, we conduct quantitative and qualitative experiments on synthetic and real-world datasets to demonstrate the effectiveness of DEGREE on node classification and graph classification tasks. | 1accept
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Title: An Interpretable Graph Generative Model with Heterophily. Abstract: Many models for graphs fall under the framework of edge-independent dot product models. These models output the probabilities of edges existing between all pairs of nodes, and the probability of a link between two nodes increases with the dot product of vectors associated with the nodes. Recent work has shown that these models are unable to capture key structures in real-world graphs, particularly heterophilous structures, wherein links occur between dissimilar nodes. We propose the first edge-independent graph generative model that is a) expressive enough to capture heterophily, b) produces nonnegative embeddings, which allow link predictions to be interpreted in terms of communities, and c) optimizes effectively on real-world graphs with gradient descent on a cross-entropy loss. Our theoretical results demonstrate the expressiveness of our model in its ability to exactly reconstruct a graph using a number of clusters that is linear in the maximum degree, along with its ability to capture both heterophily and homophily in the data. Further, our experiments demonstrate the effectiveness of our model for a variety of important application tasks such as multi-label clustering and link prediction. | 0reject
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Title: Robust Reinforcement Learning for Continuous Control with Model Misspecification. Abstract: We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms. We specifically focus on incorporating robustness into a state-of-the-art continuous control RL algorithm called Maximum a-posteriori Policy Optimization (MPO). We achieve this by learning a policy that optimizes for a worst case, entropy-regularized, expected return objective and derive a corresponding robust entropy-regularized Bellman contraction operator. In addition, we introduce a less conservative, soft-robust, entropy-regularized objective with a corresponding Bellman operator. We show that both, robust and soft-robust policies, outperform their non-robust counterparts in nine Mujoco domains with environment perturbations. In addition, we show improved robust performance on a challenging, simulated, dexterous robotic hand. Finally, we present multiple investigative experiments that provide a deeper insight into the robustness framework; including an adaptation to another continuous control RL algorithm. Performance videos can be found online at https://sites.google.com/view/robust-rl. | 1accept
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Title: Fast And Slow Learning Of Recurrent Independent Mechanisms. Abstract: Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic way to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the \textit{selected} modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules. | 1accept
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Title: An Empirical Study of Encoders and Decoders in Graph-Based Dependency Parsing. Abstract: Graph-based dependency parsing consists of two steps: first, an encoder produces a feature representation for each parsing substructure of the input sentence, which is then used to compute a score for the substructure; and second, a decoder} finds the parse tree whose substructures have the largest total score. Over the past few years, powerful neural techniques have been introduced into the encoding step which substantially increases parsing accuracies. However, advanced decoding techniques, in particular high-order decoding, have seen a decline in usage. It is widely believed that contextualized features produced by neural encoders can help capture high-order decoding information and hence diminish the need for a high-order decoder. In this paper, we empirically evaluate the combinations of different neural and non-neural encoders with first- and second-order decoders and provide a comprehensive analysis about the effectiveness of these combinations with varied training data sizes. We find that: first, when there is large training data, a strong neural encoder with first-order decoding is sufficient to achieve high parsing accuracy and only slightly lags behind the combination of neural encoding and second-order decoding; second, with small training data, a non-neural encoder with a second-order decoder outperforms the other combinations in most cases. | 2withdrawn
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Title: Autoregressive Generative Adversarial Networks. Abstract: Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones. However, it does not explicitly model feature co-occurrences in samples. In this paper, we propose a novel Autoregressive Generative Adversarial Network (ARGAN), that models the latent distribution of data using an autoregressive model, rather than relying on binary classification of samples into data/generated categories. In this way, feature co-occurrences in samples can be more efficiently captured. Our model was evaluated on two widely used datasets: CIFAR-10 and STL-10. Its performance is competitive with respect to other GAN models both quantitatively and qualitatively. | 0reject
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Title: Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering. Abstract: Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregularly-sampled time series data originates from a continuous stochastic process, the $L^2$-optimal online prediction is the conditional expectation given the currently available information. We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process. Our approach models the conditional expectation between two observations with a neural ODE and jumps whenever a new observation is made. We define a novel training framework, which allows us to prove theoretical guarantees for the first time. In particular, we show that the output of our model converges to the $L^2$-optimal prediction. This can be interpreted as solution to a special filtering problem. We provide experiments showing that the theoretical results also hold empirically. Moreover, we experimentally show that our model outperforms the baselines in more complex learning tasks and give comparisons on real-world datasets. | 1accept
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Title: On Disentangled Representations Extracted from Pretrained GANs. Abstract: Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is currently to train a generative model that separates different factors of variation in its latent space. This separation is typically enforced by training with specific regularization terms in the model's objective function. These terms, however, introduce additional hyperparameters responsible for the trade-off between disentanglement and generation quality. While tuning these hyperparameters is crucial for proper disentanglement, it is often unclear how to tune them without external supervision.
This paper investigates an alternative route to disentangled representations. Namely, we propose to extract such representations from the state-of-the-art GANs trained without disentangling terms in their objectives. This paradigm of post hoc disentanglement employs little or no hyperparameters when learning representations, while achieving results on par with existing state-of-the-art, as shown by comparison in terms of established disentanglement metrics, fairness, and the abstract reasoning task.
All our code and models are publicly available. | 0reject
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Title: MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts. Abstract: Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture distribution shifts. While valuable, the existing benchmarks are limited in that many of them only contain a small number of shifts and they lack systematic annotation about what is different across different shifts. We present MetaShift—a collection of 12,868 sets of natural images across 410 classes—to address this challenge. We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. The key construction idea is to cluster images using its metadata, which provides context for each image (e.g. “cats with cars” or “cats in bathroom”) that represent distinct data distributions. MetaShift has two important benefits: first, it contains orders of magnitude more natural data shifts than previously available. Second, it provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets. We demonstrate the utility of MetaShift in benchmarking several recent proposals for training models to be robust to data shifts. We find that the simple empirical risk minimization performs the best when shifts are moderate and no method had a systematic advantage for large shifts. We also show how MetaShift can help to visualize conflicts between data subsets during model training. | 1accept
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Title: Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit. Abstract: Recurrent Neural Networks architectures excel at processing sequences by
modelling dependencies over different timescales. The recently introduced
Recurrent Weighted Average (RWA) unit captures long term dependencies
far better than an LSTM on several challenging tasks. The RWA achieves
this by applying attention to each input and computing a weighted average
over the full history of its computations. Unfortunately, the RWA cannot
change the attention it has assigned to previous timesteps, and so struggles
with carrying out consecutive tasks or tasks with changing requirements.
We present the Recurrent Discounted Attention (RDA) unit that builds on
the RWA by additionally allowing the discounting of the past.
We empirically compare our model to RWA, LSTM and GRU units on
several challenging tasks. On tasks with a single output the RWA, RDA and
GRU units learn much quicker than the LSTM and with better performance.
On the multiple sequence copy task our RDA unit learns the task three
times as quickly as the LSTM or GRU units while the RWA fails to learn at
all. On the Wikipedia character prediction task the LSTM performs best
but it followed closely by our RDA unit. Overall our RDA unit performs
well and is sample efficient on a large variety of sequence tasks. | 0reject
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Title: Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. Abstract: Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller. | 1accept
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Title: Learning a Non-Redundant Collection of Classifiers. Abstract: Supervised learning models constructed under the i.i.d. assumption have often been shown to exploit spurious or brittle predictive signals instead of more robust ones present in the training data. Inspired by Quality-Diversity algorithms, in this work we train a collection of classifiers to learn distinct solutions to a classification problem, with the goal of learning to exploit a variety of predictive signals present in the training data. We propose an information-theoretic measure of model diversity based on minimizing an estimate of conditional total correlation of final layer representations across models given the label. We consider datasets with synthetically injected spurious correlations and evaluate our framework's ability to rapidly adapt to a change in distribution that destroys the spurious correlation. We compare our method to a variety of baselines under this evaluation protocol, showing that it is competitive with other approaches while being more successful at isolating distinct signals. We also show that our model is competitive with Invariant Risk Minimization under this evaluation protocol without requiring access to the environment information required by IRM to discriminate between spurious and robust signals. | 0reject
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Title: Planckian jitter: enhancing the color quality of self-supervised visual representations. Abstract: Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The set of used data augmentations is of crucial importance for the quality of the learned feature representation. We analyze how the traditionally used color jitter negatively impacts the quality of the color features in the learned feature representation. To address this problem, we replace this module with physics-based color augmentation, called Planckian jitter, which creates realistic variations in chromaticity, producing a model robust to llumination changes that can be commonly observed in real life, while maintaining the ability to discriminate the image content based on color information.
We further improve the performance by introducing a latent space combination of color-sensitive and non-color-sensitive features.
These are found to be complementary and the combination leads to large absolute performance gains over the default data augmentation on color classification tasks, including on Flowers-102 (+15%), Cub200 (+11%), VegFru (+15%), and T1K+ (+12%). Finally, we present a color sensitivity analysis to document the impact of different training methods on the model neurons and we show that the performance of the learned features is robust with respect to illuminant variations. | 0reject
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Title: MeshInversion: 3D textured mesh reconstruction with generative prior. Abstract: Recovering a textured 3D mesh from a single image is highly challenging, particularly for in-the-wild objects that lack 3D ground truths. Prior attempts resort to weak supervision based on 2D silhouette annotations of monocular images. Since the supervision lies in the 2D space while the output is in the 3D space, such in-direct supervision often over-emphasizes the observable part of the 3D textured mesh, at the expense of the overall reconstruction quality. Although previous attempts have adopted various hand-crafted heuristics to reduce this gap, this issue is far from being solved. In this work, we present an alternative framework, \textbf{MeshInversion}, that reduces the gap by exploiting the \textit{generative prior} of a 3D GAN pre-trained for 3D textured mesh synthesis. Reconstruction is achieved by searching for a latent space in the 3D GAN that best resembles the target mesh in accordance with the single view observation. Since the pre-trained GAN encapsulates rich 3D semantics in terms of mesh geometry and texture, searching within the GAN manifold thus naturally regularizes the realness and fidelity of the reconstruction. Importantly, such regularization is directly applied in the 3D space, providing crucial guidance of mesh parts that are unobserved in the 2D space. Experiments on standard benchmarks show that our framework obtains faithful 3D reconstructions with consistent geometry and texture across both observed and unobserved parts. Moreover, it generalizes well to meshes that are less commonly seen, such as the extended articulation of deformable objects. | 0reject
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Title: CNAS: Channel-Level Neural Architecture Search. Abstract: There is growing interest in automating designing good neural network architectures. The NAS methods proposed recently have significantly reduced architecture search cost by sharing parameters, but there is still a challenging problem of designing search space. We consider search space is typically defined with its shape and a set of operations and propose a channel-level architecture search\,(CNAS) method using only a fixed type of operation. The resulting architecture is sparse in terms of channel and has different topology at different cell. The experimental results for CIFAR-10 and ImageNet show that a fine-granular and sparse model searched by CNAS achieves very competitive performance with dense models searched by the existing methods. | 0reject
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Title: Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning. Abstract: Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distribution among clients is often non-IID in nature, making efficient optimization difficult. To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model. Instead, we consider rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. Inspired by findings from generalization literature, we employ second-order information to better understand algorithm effectiveness in FL, and find that in many cases standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects. Armed with key insights from our analysis, we propose a simple and effective method, FedAlign, to overcome data heterogeneity and the pitfalls of previous methods. FedAlign achieves comparable accuracy with state-of-the-art FL methods across a variety of settings while minimizing computation and memory overhead. | 2withdrawn
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Title: Entropy Minimization In Emergent Languages. Abstract: There is a growing interest in studying the languages emerging when neural agents are jointly trained to solve tasks requiring communication through a discrete channel. We investigate here the information-theoretic complexity of such languages, focusing on the basic two-agent, one-exchange setup. We find that, under common training procedures, the emergent languages are subject to an entropy minimization pressure that has also been detected in human language, whereby the mutual information between the communicating agent's inputs and the messages is minimized, within the range afforded by the need for successful communication. This pressure is amplified as we increase communication channel discreteness. Further, we observe that stronger discrete-channel-driven entropy minimization leads to representations with increased robustness to overfitting and adversarial attacks. We conclude by discussing the implications of our findings for the study of natural and artificial communication systems. | 0reject
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Title: Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch. Abstract: Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield huge energy efficiency benefits on neuromorphic hardware. Most existing approaches to create SNNs either convert the weights from pre-trained Artificial Neural Networks (ANNs) or directly train SNNs with surrogate gradient backpropagation. Each approach presents its pros and cons. The ANN-to-SNN conversion method requires at least hundreds of time-steps for inference to yield competitive accuracy that in turn reduces the energy savings. Training SNNs with surrogate gradients from scratch reduces the latency or total number of time-steps, but the training becomes slow/problematic and has convergence issues. Thus, the latter approach of training SNNs has been limited to shallow networks on simple datasets. To address this training issue in SNNs, we revisit batch normalization and propose a temporal Batch Normalization Through Time (BNTT) technique. Most prior SNN works till now have disregarded batch normalization deeming it ineffective for training temporal SNNs. Different from previous works, our proposed BNTT decouples the parameters in a BNTT layer along the time axis to capture the temporal dynamics of spikes. The temporally evolving learnable parameters in BNTT allow a neuron to control its spike rate through different time-steps, enabling low-latency and low-energy training from scratch. We conduct experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and event-driven DVS-CIFAR10 datasets. BNTT allows us to train deep SNN architectures from scratch, for the first time, on complex datasets with just few 25-30 time-steps. We also propose an early exit algorithm using the distribution of parameters in BNTT to reduce the latency at inference, that further improves the energy-efficiency. Code will be made available. | 0reject
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Title: Isolating Latent Structure with Cross-population Variational Autoencoders. Abstract: A significant body of recent work has examined variational autoencoders as a powerful approach for tasks which involve modeling the distribution of complex data such as images and text. In this work, we present a framework for modeling multiple data sets which come from differing distributions but which share some common latent structure. By incorporating architectural constraints and using a mutual information regularized form of the variational objective, our method successfully models differing data populations while explicitly encouraging the isolation of the shared and private latent factors. This enables our model to learn useful shared structure across similar tasks and to disentangle cross-population representations in a weakly supervised way. We demonstrate the utility of our method on several applications including image denoising, sub-group discovery, and continual learning. | 0reject
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Title: Style-based Encoder Pre-training for Multi-modal Image Synthesis. Abstract: Image-to-image (I2I) translation aims to translate images from one domain to another. To tackle the multi-modal version of I2I translation, where input and output domains have a one-to-many relation, an extra latent input is provided to the generator to specify a particular output. Recent works propose involved training objectives to learn a latent embedding, jointly with the generator, that models the distribution of possible outputs. Alternatively, we study a simple, yet powerful pre-training strategy for multi-modal I2I translation. We first pre-train an encoder, using a proxy task, to encode the style of an image, such as color and texture, into a low-dimensional latent style vector. Then we train a generator to transform an input image along with a style-code to the output domain. Our generator achieves state-of-the-art results on several benchmarks with a training objective that includes just a GAN loss and a reconstruction loss, which simplifies and speeds up the training significantly compared to competing approaches. We further study the contribution of different loss terms to learning the task of multi-modal I2I translation, and finally we show that the learned style embedding is not dependent on the target domain and generalizes well to other domains. | 0reject
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Title: Complementary-label learning for arbitrary losses and models. Abstract: In contrast to the standard classification paradigm where the true (or possibly noisy) class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label. This only specifies one of the classes that the pattern does not belong to. The seminal paper on complementary-label learning proposed an unbiased estimator of the classification risk that can be computed only from complementarily labeled data. How- ever, it required a restrictive condition on the loss functions, making it impossible to use popular losses such as the softmax cross-entropy loss. Recently, another formulation with the softmax cross-entropy loss was proposed with consistency guarantee. However, this formulation does not explicitly involve a risk estimator. Thus model/hyper-parameter selection is not possible by cross-validation— we may need additional ordinarily labeled data for validation purposes, which is not available in the current setup. In this paper, we give a novel general framework of complementary-label learning, and derive an unbiased risk estimator for arbitrary losses and models. We further improve the risk estimator by non-negative correction and demonstrate its superiority through experiments. | 0reject
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Title: Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning. Abstract: Commonsense question answering (QA) requires to mine the clues in the context to reason the answer to a question, and is a central task in natural language processing. Despite the advances of current pre-trained models, e.g. BERT, they often learn artifactual causality between the clues in context and the question because of similar but artifactual clues or highly frequent question-clue pairs in training data. To solve this issue, we propose a novel DynamIc Self-sUperviSed Erasure (DISUSE) which adaptively erases redundant and artifactual clues in the context and questions to learn and establish the correct corresponding pair relations between the questions and their clues. Specifically, DISUSE contains an \textit{erasure sampler} and a \textit{supervisor}.
The erasure sampler estimates the correlation scores between all clues and the question in an attention manner, and then erases each clue (object in image or word in question and context) according to the probability which inversely depends on its correlation score. In this way, the redundant and artifactual clues to the current question are removed, while necessary and important clues are preserved. Then the supervisor evaluates current erasure performance by inspecting whether the erased sample and its corresponding vanilla sample have consistent answer prediction distribution, and supervises the KL divergence between these two answer prediction distributions to progressively improve erasure quality in a self-supervised manner. As a result, DISUSE can learn and establish more precise corresponding question-clue pairs, and thus gives more precise answers of new questions in present of their contexts via reasoning the key and correct corresponding clues to the questions. Extensive experiment results on the RC dataset (ReClor) and VQA datasets (GQA and VQA 2.0) demonstrate the superiority of our DISUSE over the state-of-the-arts. | 2withdrawn
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Title: Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning. Abstract: Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such problems are commonly solved by well-known game-theoretic criteria, such as Shapley value or Banzhaf value. In this work, we present a novel energy-based treatment for cooperative games, with a theoretical justification by the maximum entropy framework. Surprisingly, by conducting variational inference of the energy-based model, we recover various game-theoretic valuation criteria through conducting one-step fixed point iteration for maximizing the mean-field ELBO objective. This observation also verifies the rationality of existing criteria, as they are all attempting to decouple the correlations among the players through the mean-field approach. By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index. We prove that under uniform initializations, these variational valuations all satisfy a set of game-theoretic axioms. We experimentally demonstrate that the proposed Variational Index enjoys lower decoupling error and better valuation performance on certain synthetic and real-world valuation problems. | 1accept
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Title: Concentric Spherical GNN for 3D Representation Learning. Abstract: Learning 3D representations of point clouds that generalize well to arbitrary orientations is a challenge of practical importance in problems ranging from computer vision to molecular modeling.
The proposed approach is based on a concentric spherical representation of 3D space, formed by nesting spatially-sampled spheres resulting from the highly regular icosahedral discretization.
We propose separate intra-sphere and inter-sphere convolutions over the resulting concentric spherical grid, which are combined into a convolutional framework for learning volumetric and rotationally equivariant representations over point clouds.
We demonstrate the effectiveness of our approach for 3D object classification, and towards resolving the electronic structure of atomistic systems. | 0reject
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Title: Continual Learning via Low-Rank Network Updates. Abstract: Continual learning seeks to train a single network for multiple tasks (one after another), where training data for each task is only available during the training of that task. Neural networks tend to forget older tasks when they are trained for the newer tasks; this property is often known as catastrophic forgetting. To address this issue, continual learning methods use episodic memory, parameter regularization, masking and pruning, or extensible network structures. In this paper, we propose a new continual learning framework based on low-rank factorization. In particular, we represent the network weights for each layer as a linear combination of several low-rank (or rank-1) matrices. To update the network for a new task, we learn a low-rank (or rank-1) matrix and add that to the weights of every layer. We also introduce an additional selector vector that assigns different weights to the low-rank matrices learned for the previous tasks. We show that our approach performs better than the current state-of-the-art methods in terms of accuracy and forgetting. Our method also offers better memory efficiency compared to episodic memory-based approaches. | 2withdrawn
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Title: On Characterizing the Capacity of Neural Networks Using Algebraic Topology. Abstract: The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this paper, we reframe the problem of architecture selection as understanding how data determines the most expressive and generalizable architectures suited to that data, beyond inductive bias. After suggesting algebraic topology as a measure for data complexity, we show that the power of a network to express the topological complexity of a dataset in its decision boundary is a strictly limiting factor in its ability to generalize. We then provide the first empirical characterization of the topological capacity of neural networks. Our empirical analysis shows that at every level of dataset complexity, neural networks exhibit topological phase transitions and stratification. This observation allowed us to connect existing theory to empirically driven conjectures on the choice of architectures for a single hidden layer neural networks. | 0reject
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Title: Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets. Abstract: Achieving accurate spatio-temporal predictions in large-scale systems is extremely valuable in many real-world applications, such as weather forecasts, retail forecasting, and urban traffic forecasting. So far, most existing methods for multi-horizon, multi-task and multi-target predictions select important predicting variables via their correlations with responses, and thus it is highly possible that many forecasting models generated from those methods are not causal, leading to poor interpretability. The aim of this paper is to develop a collaborative causal spatio-temporal fusion transformer, named CausalTrans, to establish the collaborative causal effects of predictors on multiple forecasting targets, such as supply and demand in ride-sharing platforms. Specifically, we integrate the causal attention with the Conditional Average Treatment Effect (CATE) estimation method for causal inference. Moreover, we propose a novel and fast multi-head attention evolved from Taylor expansion instead of softmax, reducing time complexity from $O(\mathcal{V}^2)$ to $O(\mathcal{V})$, where $\mathcal{V}$ is the number of nodes in a graph. We further design a spatial graph fusion mechanism to significantly reduce the parameters' scale. We conduct a wide range of experiments to demonstrate the interpretability of causal attention, the effectiveness of various model components, and the time efficiency of our CausalTrans. As shown in these experiments, our CausalTrans framework can achieve up to 15$\%$ error reduction compared with various baseline methods. | 0reject
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Title: Expected Tight Bounds for Robust Deep Neural Network Training. Abstract: Training Deep Neural Networks (DNNs) that are robust to norm bounded adversarial attacks remains an elusive problem. While verification based methods are generally too expensive to robustly train large networks, it was demonstrated by Gowal et. al. that bounded input intervals can be inexpensively propagated from layer to layer through deep networks. This interval bound propagation (IBP) approach led to high robustness and was the first to be employed on large networks. However, due to the very loose nature of the IBP bounds, particularly for large/deep networks, the required training procedure is complex and involved. In this paper, we closely examine the bounds of a block of layers composed of an affine layer, followed by a ReLU, followed by another affine layer. To this end, we propose \emph{expected} bounds (true bounds in expectation), which are provably tighter than IBP bounds in expectation. We then extend this result to deeper networks through blockwise propagation and show that we can achieve orders of magnitudes tighter bounds compared to IBP. Using these tight bounds, we demonstrate that a simple standard training procedure can achieve impressive robustness-accuracy trade-off across several architectures on both MNIST and CIFAR10. | 0reject
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