pwc-archive/papers-with-abstracts huggingface dataset:
Instance-wise Feature Grouping
Authors: Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: General Classification

In many learning problems, the domain scientist is often interested in discovering the groups of features that are redundant and are important for classification.  Moreover, the features that belong to each group, and the important feature groups may vary per sample.  But what do we mean by feature redundancy?  In this paper, we formally define two types of redundancies using information theory:  \textit{Representation} and \textit{Relevant redundancies}.  We leverage these redundancies to design a formulation for instance-wise feature group discovery and reveal a theoretical guideline to help discover the appropriate number of groups.  We approximate mutual information via a variational lower bound and learn the feature group and selector indicators with Gumbel-Softmax in optimizing our formulation. Experiments on synthetic data validate our theoretical claims. Experiments on MNIST, Fashion MNIST, and gene expression datasets show that our method discovers feature groups with high classification accuracies.
Group Contextual Encoding for 3D Point Clouds
Authors: Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Scene Understanding

Global context is crucial for 3D point cloud scene understanding tasks. In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.  The encoding layer learns a set of code words in the feature space of the 3D point cloud to characterize the global semantic context, and then based on these code words, the method learns a global contextual descriptor to reweight the featuremaps accordingly. Moreover, compared to 2D scenarios, data sparsity becomes a major issue in 3D point cloud scenarios, and the performance of contextual encoding quickly saturates when the number of code words increases. To mitigate this problem, we further proposed a group contextual encoding method, which divides the channel into groups and then performs encoding on group-divided feature vectors. This method facilitates learning of global context in grouped subspace for 3D point clouds. We evaluate the effectiveness and generalizability of our method on three widely-studied 3D point cloud tasks. Experimental results have shown that the proposed method outperformed the VoteNet  remarkably with 3 mAP on the benchmark of SUN-RGBD, with the metrics of mAP@ 0.25, and a much greater margin of 6.57 mAP on  ScanNet   with the metrics of  mAP@ 0.5. Compared to the baseline of PointNet++,  the proposed method leads to an accuracy of 86 %, outperforming the baseline by 1.5 %. Our proposed method have outperformed the non-grouping baseline methods across the board  and establishes new state-of-the-art on these benchmarks.
Is normalization indispensable for training deep neural network?
Authors: Jie Shao, Kai Hu, Changhu Wang, xiangyang xue, Bhiksha Raj
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: General Classification, image-classification, Image Classification, Machine Translation, object-detection, Object Detection, Translation, Video Classification

Normalization operations are widely used to train deep neural networks, and they can improve both convergence and generalization in most tasks. The theories for normalization's effectiveness and new forms of normalization have always been hot topics in research. To better understand normalization, one question can be whether normalization is indispensable for training deep neural network? In this paper, we study what would happen when normalization layers are removed from the network, and show how to train deep neural networks without normalization layers and without performance degradation. Our proposed method can achieve the same or even slightly better performance in a variety of tasks: image classification in ImageNet, object detection and segmentation in MS-COCO, video classification in Kinetics, and machine translation in WMT English-German, etc. Our study may help better understand the role of normalization layers and can be a competitive alternative to normalization layers. Codes are available.
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Authors: Ziqi Ke, Haris Vikalo
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Clustering, Dimensionality Reduction

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods. Source codes are available at https://github.com/WuLoli/CAECseq.
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Authors: Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning (RL)

Many recent reinforcement learning (RL) methods learn stochastic policies with entropy regularization for exploration and robustness. However, in continuous action spaces, integrating entropy regularization with expressive policies is challenging and usually requires complex inference procedures. To tackle this problem, we propose a novel regularization method that is compatible with a broad range of expressive policy architectures. An appealing feature is that, the estimation of our regularization terms is simple and efficient even when the policy distributions are unknown. We show that our approach can effectively promote the exploration in continuous action spaces. Based on our regularization, we propose an off-policy actor-critic algorithm. Experiments demonstrate that the proposed algorithm outperforms state-of-the-art regularized RL methods in continuous control tasks.
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms
Authors: Sascha Saralajew, Lars Holdijk, Thomas Villmann
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Adversarial Robustness, Quantization, Triplet

Methods for adversarial robustness certification aim to provide an upper bound on the test error of a classifier under adversarial manipulation of its input. Current certification methods are computationally expensive and limited to attacks that optimize the manipulation with respect to a norm. We overcome these limitations by investigating the robustness properties of Nearest Prototype Classifiers (NPCs) like learning vector quantization and large margin nearest neighbor. For this purpose, we study the hypothesis margin. We prove that if NPCs use a dissimilarity measure induced by a seminorm, the hypothesis margin is a tight lower bound on the size of adversarial attacks and can be calculated in constant time—this provides the first adversarial robustness certificate calculable in reasonable time. Finally, we show that each NPC trained by a triplet loss maximizes the hypothesis margin and is therefore optimized for adversarial robustness. In the presented evaluation, we demonstrate that NPCs optimized for adversarial robustness are competitive with state-of-the-art methods and set a new benchmark with respect to computational complexity for robustness certification.
A Local Temporal Difference Code for Distributional Reinforcement Learning
Authors: Pablo Tano, Peter Dayan, Alexandre Pouget
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Distributional Reinforcement Learning, Imputation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent theoretical and experimental results suggest that the dopamine system implements distributional temporal difference backups, allowing learning of the entire distributions of the long-run values of states rather than just their expected values. However, the distributional codes explored so far rely on a complex imputation step which crucially relies on spatial non-locality: in order to compute reward prediction errors, units must know not only their own state but also the states of the other units. It is far from clear how these steps could be implemented in realistic neural circuits. Here, we introduce the Laplace code: a local temporal difference code for distributional reinforcement learning that is representationally powerful and computationally straightforward. The code decomposes value distributions and prediction errors across three separated dimensions: reward magnitude (related to distributional quantiles), temporal discounting (related to the Laplace transform of future rewards) and time horizon (related to eligibility traces). Besides lending itself to a local learning rule, the decomposition recovers the temporal evolution of the immediate reward distribution, indicating all possible rewards at all future times. This increases representational capacity and allows for temporally-flexible computations that immediately adjust to changing horizons or discount factors.
The Potts-Ising model for discrete multivariate data
Authors: Zahra Razaee, Arash Amini
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12

Modeling dependencies in multivariate discrete data is a challenging problem, especially in high dimensions. The Potts model is a versatile such model, suitable when each coordinate is a categorical variable. However, the full Potts model has too many parameters to be accurately fit when the number of categories is large. We introduce a variation on the Potts model that allows for general categorical marginals and Ising-type multivariate dependence. This reduces the number of parameters from $\Omega(d^2 K^2)$ in the full Potts model to $O(d^2 + Kd)$, where $K$ is the number of categories and $d$ is the dimension of the data. We show that the complexity of fitting this new Potts-Ising model is the same as that of an Ising model. In particular, adopting the neighborhood regression framework, the model can be fit by solving $d$ separate logistic regressions. We demonstrate the ability of the model to capture multivariate dependencies by comparing with existing approaches.
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
Authors: Shailee Jain, Vy Vo, Shivangi Mahto, Amanda LeBel, Javier S. Turek, Alexander Huth
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12

Natural language contains information at multiple timescales. To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs). However, these LM-derived representations do not explicitly separate information at different timescales, making it difficult to interpret the encoding models. In this work we construct interpretable multi-timescale representations by forcing individual units in an LSTM LM to integrate information over specific temporal scales. This allows us to explicitly and directly map the timescale of information encoded by each individual fMRI voxel. Further, the standard fMRI encoding procedure does not account for varying temporal properties in the encoding features. We modify the procedure so that it can capture both short- and long-timescale information. This approach outperforms other encoding models, particularly for voxels that represent long-timescale information. It also provides a finer-grained map of timescale information in the human language pathway. This serves as a framework for future work investigating temporal hierarchies across artificial and biological language systems.
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis
Authors: Gang Wang, Songtao Lu, Georgios Giannakis, Gerald Tesauro, Jian Sun
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12

The present contribution deals with decentralized policy evaluation in multi-agent Markov decision processes using temporal-difference (TD) methods with linear function approximation for scalability. The agents cooperate to estimate the value function of such a process by observing continual state transitions of a shared environment over the graph of interconnected nodes (agents), along with locally private rewards. Different from existing consensus-type TD algorithms, the approach here develops a simple decentralized TD tracker by wedding TD learning with gradient tracking techniques. The non-asymptotic properties of the novel TD tracker are established for both independent and identically distributed (i.i.d.) as well as Markovian transitions through a unifying multistep Lyapunov analysis. In contrast to the prior art, the novel algorithm forgoes the limiting error bounds on the number of agents, which endows it with performance comparable to that of centralized TD methods that are the sharpest known to date.
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Authors: Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12

Recently, Transformer networks have redefined the state of the art in many NLP tasks. However, these models suffer from quadratic computational cost in the input sequence length $n$ to compute pairwise attention in each layer. This has prompted recent research into sparse Transformers that sparsify the connections in the attention layers. While empirically promising for long sequences, fundamental questions remain unanswered: Can sparse Transformers approximate any arbitrary sequence-to-sequence function, similar to their dense counterparts? How does the sparsity pattern and the sparsity level affect their performance? In this paper, we address these questions and provide a unifying framework that captures existing sparse attention models. We propose sufficient conditions under which we prove that a sparse attention model can universally approximate any sequence-to-sequence function. Surprisingly, our results show that sparse Transformers with only $O(n)$ connections per attention layer can approximate the same function class as the dense model with $n^2$ connections. Lastly, we present experiments comparing different patterns/levels of sparsity on standard NLP tasks.
Identifying signal and noise structure in neural population activity with Gaussian process factor models
Authors: Stephen Keeley, Mikio Aoi, Yiyi Yu, Spencer Smith, Jonathan W. Pillow
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Variational Inference

Neural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis of such datasets is to characterize systematic aspects of neural activity that carry information about the repeated stimulus or behavior of interest, which can be considered signal'', and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considerednoise''. Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data. However, they have not yet been adapted to the problem of characterizing signal and noise in multi-trial datasets. Here we address this shortcoming by proposing ``signal-noise'' Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity. To learn the parameters of our model, we introduce a Fourier-domain black box variational inference method that quickly identifies smooth latent structure. The resulting model reliably uncovers latent signal and trial-to-trial noise-related fluctuations in large-scale recordings. We use this model to show that in monkey V1, noise fluctuations perturb neural activity within a subspace orthogonal to signal activity, suggesting that trial-by-trial noise does not interfere with signal representations. Finally, we extend the model to capture statistical dependencies across brain regions in multi-region data. We show that in mouse visual cortex, models with shared noise across brain regions out-perform models with independent per-region noise.
Equivariant Networks for Hierarchical Structures
Authors: Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Semantic Segmentation, Translation

While using invariant and equivariant maps, it is possible to apply deep learning to a range of primitive data structures, a formalism for dealing with hierarchy is lacking. This is a significant issue because many practical structures are hierarchies of simple building blocks; some examples include sequences of sets, graphs of graphs, or multiresolution images. Observing that the symmetry of a hierarchical structure is the ``wreath product'' of symmetries of the building blocks, we express the equivariant map for the hierarchy using an intuitive combination of the equivariant linear layers of the building blocks. More generally, we show that any equivariant map for the hierarchy has this form. To demonstrate the effectiveness of this approach to model design, we consider its application in the semantic segmentation of point-cloud data. By voxelizing the point cloud, we impose a hierarchy of translation and permutation symmetries on the data and report state-of-the-art on {semantic3d}, {s3dis}, and {vkitti}, that include some of the largest real-world point-cloud benchmarks.
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
Authors: Akash Saha, Balamurugan Palaniappan
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12

Operator-valued kernels have shown promise in supervised learning problems with functional inputs and functional outputs. The crucial (and possibly restrictive) assumption of positive definiteness of operator-valued kernels has been instrumental in developing efficient algorithms. In this work, we consider operator-valued kernels which might not be necessarily positive definite. To tackle the indefiniteness of operator-valued kernels, we harness the machinery of Reproducing Kernel Krein Spaces (RKKS) of function-valued functions. A representer theorem is illustrated which yields a suitable loss stabilization problem for supervised learning with function-valued inputs and outputs. Analysis of generalization properties of the proposed framework is given. An iterative Operator based Minimum Residual (OpMINRES) algorithm is proposed for solving the loss stabilization problem. Experiments with indefinite operator-valued kernels on synthetic and real data sets demonstrate the utility of the proposed approach.
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
Authors: Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael Jordan
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)


Reinforcement learning (RL) algorithms combined with modern function approximators such as kernel functions and deep neural networks have achieved significant empirical successes in large-scale application problems with 
 a massive number of states.
 From a theoretical perspective, however, 
 RL with functional approximation  poses a fundamental challenge  to developing algorithms  with provable computational and statistical efficiency, 
 due to the need to take into consideration  both the exploration-exploitation tradeoff that is inherent in RL and the bias-variance tradeoff that is innate in statistical estimation. 
 To address such a challenge,  focusing on the episodic setting where the action-value functions are represented by a kernel function or over-parametrized neural network,
we propose the first provable RL algorithm with both polynomial runtime and sample complexity, without additional assumptions on the data-generating model. 
In particular, for both the kernel and neural settings,  we prove that an optimistic modification of the least-squares value iteration algorithm incurs an $\tilde{\mathcal{O}}(\delta_{\cF}  H^2 \sqrt{T})$ regret, where $\delta_{\cF}$ characterizes the intrinsic complexity of the function class $\cF$, $H$ is the length of each episode, and  $T$ is the total number of episodes. 
Our regret bounds are independent of the number of states and therefore even allows it to diverge,  which exhibits the benefit of function approximation.
RANet: Region Attention Network for Semantic Segmentation
Authors: Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin
Date: 2020-12-01 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Object, Segmentation, Semantic Segmentation

Recent semantic segmentation methods model the relationship between pixels to construct the contextual representations. In this paper, we introduce the \emph{Region Attention Network} (RANet), a novel attention network for modeling the relationship between object regions. RANet divides the image into object regions, where we select representative information. In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image. We train the construction of object regions, the selection of the representative regional contents, the configuration of information pathways and the context exchange between pixels, jointly, to improve the segmentation accuracy. We extensively evaluate our method on the challenging segmentation benchmarks, demonstrating that RANet effectively helps to achieve the state-of-the-art results.