pwc-archive/papers-with-abstracts huggingface dataset:
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
Authors: Si-Qi Liu, Milos Hauskrecht
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Gaussian Processes, Point Processes

Real-world event sequences consist of complex mixtures of different types of events occurring in time. An event may depend on past events of the same type, as well as, the other types. Point processes define a general class of models for event sequences. ``Regressive point processes'' refer to point processes that directly model the dependency between an event and any past event, an example of which is a Hawkes process. In this work, we propose and develop a new nonparametric regressive point process model based on Gaussian processes. We show that our model can represent better many commonly observed real-world event sequences and capture the dependencies between events that are difficult to model using existing nonparametric Hawkes process variants. We demonstrate the improved predictive performance of our model against state-of-the-art baselines on multiple synthetic and real-world datasets.
Deep Supervised Summarization: Algorithm and Application to Learning Instructions
Authors: Chengguang Xu, Ehsan Elhamifar
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Representation Learning

We address the problem of finding representative points of datasets by learning from multiple datasets and their ground-truth summaries. We develop a supervised subset selection framework, based on the facility location utility function, which learns to map datasets to their ground-truth representatives. To do so, we propose to learn representations of data so that the input of transformed data to the facility location recovers their ground-truth representatives. Given the NP-hardness of the utility function, we consider its convex relaxation based on sparse representation and investigate conditions under which the solution of the convex optimization recovers ground-truth representatives of each dataset. We design a loss function whose minimization over the parameters of the data representation network leads to satisfying the theoretical conditions, hence guaranteeing recovering ground-truth summaries. Given the non-convexity of the loss function, we develop an efficient learning scheme that alternates between representation learning by minimizing our proposed loss given the current assignments of points to ground-truth representatives and updating assignments given the current data representation. By experiments on the problem of learning key-steps (subactivities) of instructional videos, we show that our proposed framework improves the state-of-the-art supervised subset selection algorithms.
Reconciling λ-Returns with Experience Replay
Authors: Brett Daley, Christopher Amato
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Atari Games, Deep Reinforcement Learning, Incremental Learning, Reinforcement Learning

Modern deep reinforcement learning methods have departed from the incremental learning required for eligibility traces, rendering the implementation of the λ-return difficult in this context. In particular, off-policy methods that utilize experience replay remain problematic because their random sampling of minibatches is not conducive to the efficient calculation of λ-returns. Yet replay-based methods are often the most sample efficient, and incorporating λ-returns into them is a viable way to achieve new state-of-the-art performance. Towards this, we propose the first method to enable practical use of λ-returns in arbitrary replay-based methods without relying on other forms of decorrelation such as asynchronous gradient updates. By promoting short sequences of past transitions into a small cache within the replay memory, adjacent λ-returns can be efficiently precomputed by sharing Q-values. Computation is not wasted on experiences that are never sampled, and stored λ-returns behave as stable temporal-difference (TD) targets that replace the target network. Additionally, our method grants the unique ability to observe TD errors prior to sampling; for the first time, transitions can be prioritized by their true significance rather than by a proxy to it. Furthermore, we propose the novel use of the TD error to dynamically select λ-values that facilitate faster learning. We show that these innovations can enhance the performance of DQN when playing Atari 2600 games, even under partial observability. While our work specifically focuses on λ-returns, these ideas are applicable to any multi-step return estimator.
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence
Authors: Fengxiang He, Tongliang Liu, DaCheng Tao
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Deep neural networks have received dramatic success based on the optimization method of stochastic gradient descent (SGD). However, it is still not clear how to tune hyper-parameters, especially batch size and learning rate, to ensure good generalization. This paper reports both theoretical and empirical evidence of a training strategy that we should control the ratio of batch size to learning rate not too large to achieve a good generalization ability. Specifically, we prove a PAC-Bayes generalization bound for neural networks trained by SGD, which has a positive correlation with the ratio of batch size to learning rate. This correlation builds the theoretical foundation of the training strategy. Furthermore, we conduct a large-scale experiment to verify the correlation and training strategy. We trained 1,600 models based on architectures ResNet-110, and VGG-19 with datasets CIFAR-10 and CIFAR-100 while strictly control unrelated variables. Accuracies on the test sets are collected for the evaluation. Spearman's rank-order correlation coefficients and the corresponding $p$ values on 164 groups of the collected data demonstrate that the correlation is statistically significant, which fully supports the training strategy.
Combinatorial Inference against Label Noise
Authors: Paul Hongsuck Seo, Geeho Kim, Bohyung Han
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Clustering

Label noise is one of the critical sources that degrade generalization performance of deep neural networks significantly. To handle the label noise issue in a principled way, we propose a unique classification framework of constructing multiple models in heterogeneous coarse-grained meta-class spaces and making joint inference of the trained models for the final predictions in the original (base) class space. Our approach reduces noise level by simply constructing meta-classes and improves accuracy via combinatorial inferences over multiple constituent classifiers. Since the proposed framework has distinct and complementary properties for the given problem, we can even incorporate additional off-the-shelf learning algorithms to improve accuracy further. We also introduce techniques to organize multiple heterogeneous meta-class sets using $k$-means clustering and identify a desirable subset leading to learn compact models. Our extensive experiments demonstrate outstanding performance in terms of accuracy and efficiency compared to the state-of-the-art methods under various synthetic noise configurations and in a real-world noisy dataset.
Cross-channel Communication Networks
Authors: Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decoder

Convolutional neural networks process input data by sending channel-wise feature response maps to subsequent layers. While a lot of progress has been made by making networks deeper, information from each channel can only be propagated from lower levels to higher levels in a hierarchical feed-forward manner. When viewing each filter in the convolutional layer as a neuron, those neurons are not communicating explicitly within each layer in CNNs. We introduce a novel network unit called Cross-channel Communication (C3) block, a simple yet effective module to encourage the neuron communication within the same layer. The C3 block enables neurons to exchange information through a micro neural network, which consists of a feature encoder, a message communicator, and a feature decoder, before sending the information to the next layer. With C3 block, each neuron accounts for the channel-wise responses from other neurons at the same layer and learns more discriminative and complementary representations. Extensive experiments for multiple computer vision tasks show that our proposed mechanism allows shallower networks to aggregate useful information within each layer, and performances outperform baseline deep networks and other competitive methods.
Twin Auxilary Classifiers GAN
Authors: Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Conditional Image Generation, Diversity, Image Generation

Conditional generative models enjoy significant progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases. In this paper, we identify the source of low diversity issue theoretically and propose a practical solution to the problem. We show that the auxiliary classifier in AC-GAN imposes perfect separability, which is disadvantageous when the supports of the class distributions have significant overlap. To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that adds a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that our TAC-GAN can effectively minimize the divergence between generated and real data distributions. Extensive experimental results show that our TAC-GAN can successfully replicate the true data distributions on simulated data, and significantly improves the diversity of class-conditional image generation on real datasets.
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Authors: Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Graph Embedding, Translation

Graph embedding has been intensively studied recently, due to the advance of various neural network models. Theoretical analyses and empirical studies have pushed forward the translation of discrete graph structures into distributed representation vectors, but seldom considered the reverse direction, i.e., generation of graphs from given related context spaces. Particularly, since graphs often become more meaningful when associated with semantic contexts (e.g., social networks of certain communities, gene networks of certain diseases), the ability to infer graph structures according to given semantic conditions could be of great value. While existing graph generative models only consider graph structures without semantic contexts, we formulate the novel problem of conditional structure generation, and propose a novel unified model of graph variational generative adversarial nets (CondGen) to handle the intrinsic challenges of flexible context-structure conditioning and permutation-invariant generation. Extensive experiments on two deliberately created benchmark datasets of real-world context-enriched networks demonstrate the supreme effectiveness and generalizability of CondGen.
Deliberative Explanations: visualizing network insecurities
Authors: Pei Wang, Nuno Nvasconcelos
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Attribute, Object Recognition, Scene Classification

A new approach to explainable AI, denoted {\it deliberative explanations,\/}
  is proposed. Deliberative explanations are a visualization technique
  that aims to go beyond the simple visualization of the image regions
  (or, more generally, input variables) responsible for a network
  prediction. Instead, they aim to expose the deliberations carried
  by the network to arrive at that prediction, by uncovering the
  insecurities of the network about the latter. The
  explanation consists of a list of insecurities, each composed of
  1) an image region (more generally, a set of input variables), and 2)
  an ambiguity formed by the pair of classes responsible for the network
  uncertainty about the region. Since insecurity detection requires
  quantifying the difficulty of network predictions, deliberative
  explanations combine ideas from the literatures on visual explanations and
  assessment of classification difficulty. More specifically,
  the proposed implementation
  combines attributions with respect to both class
  predictions and a difficulty score.
  An evaluation protocol that leverages object recognition (CUB200)
  and scene classification (ADE20K) datasets that combine part and
  attribute annotations is also introduced to evaluate the accuracy of
  deliberative explanations. Finally, an experimental evaluation shows that
  the most accurate explanations are achieved by combining non self-referential
  difficulty scores and second-order attributions. The resulting
  insecurities are shown to correlate with regions of attributes that
  are shared by different classes. Since these regions are also ambiguous
  for humans, deliberative explanations are intuitive, suggesting that
  the deliberative process of modern networks correlates with human
  reasoning.
Neural Diffusion Distance for Image Segmentation
Authors: Jian Sun, Zongben Xu
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Diffusion distance is a spectral method for measuring distance among nodes on graph considering global data structure. In this work, we propose a spec-diff-net for computing diffusion distance on graph based on approximate spectral decomposition. The network is a differentiable deep architecture consisting of  feature extraction and diffusion distance modules for computing diffusion distance on image by end-to-end training.  We design low resolution kernel matching loss and high resolution segment matching loss to enforce the network's output to be consistent with human-labeled image segments.  To compute high-resolution diffusion distance or segmentation mask, we design an up-sampling strategy by feature-attentional interpolation which can be learned when training spec-diff-net. With the learned diffusion distance, we propose a hierarchical image segmentation method outperforming previous segmentation methods. Moreover, a weakly supervised semantic segmentation network is designed using diffusion distance and achieved promising results on PASCAL VOC 2012 segmentation dataset.
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions
Authors: Chris Russell, Matteo Toso, Neill Campbell
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: 3D Reconstruction

We present a new technique for the learning of continuous energy functions that
we refer to as Wibergian Learning. One common approach to inverse problems
is to cast them as an energy minimisation problem, where the minimum cost
solution found is used as an estimator of hidden parameters. Our new approach
formally characterises the dependency between weights that control the shape of
the energy function, and the location of minima, by describing minima as fixed
points of optimisation methods. This allows for the use of gradient-based end-to-
end training to integrate deep-learning and the classical inverse problem methods.
We show how our approach can be applied to obtain state-of-the-art results in the
diverse applications of tracker fusion and multiview 3D reconstruction.
Expressive power of tensor-network factorizations for probabilistic modeling
Authors: Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, Ignacio Cirac
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Tensor Networks

Tensor-network techniques have recently proven useful in machine learning, both as a tool for the formulation of new learning algorithms and for enhancing the mathematical understanding of existing methods. Inspired by these developments, and the natural correspondence between tensor networks and probabilistic graphical models, we provide a rigorous analysis of the expressive power of various tensor-network factorizations of discrete multivariate probability distributions. These factorizations include non-negative tensor-trains/MPS, which are in correspondence with hidden Markov models, and Born machines, which are naturally related to the probabilistic interpretation of quantum circuits. When used to model probability distributions, they exhibit tractable likelihoods and admit efficient learning algorithms. Interestingly, we prove that there exist probability distributions for which there are unbounded separations between the resource requirements of some of these tensor-network factorizations. Of particular interest, using complex instead of real tensors can lead to an arbitrarily large reduction in the number of parameters of the network. Additionally, we introduce locally purified states (LPS), a new factorization inspired by techniques for the simulation of quantum systems, with provably better expressive power than all other representations considered. The ramifications of this result are explored through numerical experiments.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
Authors: Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obvious existence of such complex relationships in many real-world networks naturaly motivates the problem of learning with hypergraphs. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, we propose HyperGCN, a novel GCN for SSL on attributed hypergraphs. Additionally, we show how HyperGCN can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems. We demonstrate HyperGCN's effectiveness through detailed experimentation on real-world hypergraphs. We have made HyperGCN's source code available to foster reproducible research.
Efficient Meta Learning via Minibatch Proximal Update
Authors: Pan Zhou, Xiao-Tong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Few-Shot Learning, Meta-Learning

We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks. A particularly simple yet successful paradigm for this research is model-agnostic meta-learning (MAML). Implementation and analysis of MAML, however, can be tricky; first-order approximation is usually adopted to avoid directly computing Hessian matrix but as a result the convergence and generalization guarantees remain largely mysterious for MAML. To remedy this deficiency, in this paper we propose a minibatch proximal update based meta-learning approach for learning to efficient hypothesis transfer. The principle is to learn a prior hypothesis shared across tasks such that the minibatch risk minimization biased regularized by this prior can quickly converge to the optimal hypothesis in each training task. The prior hypothesis training model can be efficiently optimized via SGD with provable convergence guarantees for both convex and non-convex problems. Moreover, we theoretically justify the benefit of the learnt prior hypothesis for fast adaptation to new few-shot learning tasks via minibatch proximal update. Experimental results on several few-shot regression and classification tasks demonstrate the advantages of our method over state-of-the-arts.
Guided Similarity Separation for Image Retrieval
Authors: Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Clustering, Image Retrieval, Retrieval

Despite recent progress in computer vision, image retrieval remains a challenging open problem. Numerous variations such as view angle, lighting and occlusion make it difficult to design models that are both robust and efficient. Many leading methods traverse the nearest neighbor graph to exploit higher order neighbor information and uncover the highly complex underlying manifold. In this work we propose a different approach where we leverage graph convolutional networks to directly encode neighbor information into image descriptors. We further leverage ideas from clustering and manifold learning, and introduce an unsupervised loss based on pairwise separation of image similarities. Empirically, we demonstrate that our model is able to successfully learn a new descriptor space that significantly improves retrieval accuracy, while still allowing efficient inner product inference. Experiments on five public benchmarks show highly competitive performance with up to 24\% relative improvement in mAP over leading baselines. Full code for this work is available here: https://github.com/layer6ai-labs/GSS.
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes
Authors: Rui Li
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Gaussian Processes

This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space. We propose a unified nonstationary modeling framework to jointly encode the observation correlations to generate a piece-wise representation with a hyper-level Gaussian process (GP) governing the overall contour of the pieces. In particular, we couple the encoding process with automatic relevance determination (ARD) to promote sparsity to account for the inherent redundancy. The hyper GP enables us to share statistical strength among the observation variables over a collection of GPs defined within the observation pieces to characterize the variables' respective local smoothness. Experiments conducted across domains show superior performances over the state-of-the-art methods.
Fast Sparse Group Lasso
Authors: Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Sparse Group Lasso is a method of linear regression analysis that finds sparse parameters in terms of both feature groups and individual features.
Block Coordinate Descent is a standard approach to obtain the parameters of Sparse Group Lasso, and iteratively updates the parameters for each parameter group.
However, as an update of only one parameter group depends on all the parameter groups or data points, the computation cost is high when the number of the parameters or data points is large.
This paper proposes a fast Block Coordinate Descent for Sparse Group Lasso.
It efficiently skips the updates of the groups whose parameters must be zeros by using the parameters in one group.
In addition, it preferentially updates parameters in a candidate group set, which contains groups whose parameters must not be zeros.
Theoretically, our approach guarantees the same results as the original Block Coordinate Descent.
Experiments show that our algorithm enhances the efficiency of the original algorithm without any loss of accuracy.