pwc-archive/papers-with-abstracts huggingface dataset:
HR-TD: A Regularized TD Method to Avoid Over-Generalization
Authors: Ishan Durugkar, Bo Liu, Peter Stone
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Temporal Difference learning with function approximation has been widely used recently and has led to several successful results.  However, compared with the original tabular-based methods, one major drawback of temporal difference learning with neural networks and other function approximators is that they tend to over-generalize across temporally successive states, resulting in slow convergence and even instability. In this work, we propose a novel TD learning method, Hadamard product Regularized TD (HR-TD), that reduces over-generalization and thus leads to faster convergence. This approach can be easily applied to both linear and nonlinear function approximators. 
HR-TD is evaluated on several linear and nonlinear benchmark domains, where we show improvement in learning behavior and performance.
Multi-Objective Value Iteration with Parameterized Threshold-Based Safety Constraints
Authors: Hussein Sibai, Sayan Mitra
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We consider an environment with multiple reward functions. One of them represents goal achievement and the others represent instantaneous safety conditions. We consider a scenario where the safety rewards should always be above some thresholds. The thresholds are parameters with values that differ between users.
%The thresholds are not known at the time the policy is being designed.
We efficiently compute a family of policies that cover all threshold-based constraints and maximize the goal achievement reward. We introduce a new parameterized threshold-based scalarization method of the reward vector that encodes our objective. We present novel data structures to store the value functions of the Bellman equation that allow their efficient computation using the value iteration algorithm. We present results for both discrete and continuous state spaces.
Optimizing for Generalization in Machine Learning with Cross-Validation Gradients
Authors: Barratt, Shane, Sharma, Rishi
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning, Hyperparameter Optimization, regression

Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance. In this paper, we show that the cross-validation risk is differentiable with respect to the hyperparameters and training data for many common machine learning algorithms, including logistic regression, elastic-net regression, and support vector machines. Leveraging this property of differentiability, we propose a cross-validation gradient method (CVGM) for hyperparameter optimization. Our method enables efficient optimization in high-dimensional hyperparameter spaces of the cross-validation risk, the best surrogate of the true generalization ability of our learning algorithm.
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
Authors: Masanori Yamada, Kim Heecheol, Kosuke Miyoshi, Hiroshi Yamakawa
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Disentanglement, Representation Learning

A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and transferable representations from data. We focused on the disentangled representation of sequential data because there is a wide range of potential applications if disentanglement representation is extended to sequential data such as video, speech, and stock price data. Sequential data is characterized by dynamic factors and static factors: dynamic factors are time-dependent, and static factors are independent of time. Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors. However, this model can not disentangle representations between dynamic factors, such as disentangling ”picking” and ”throwing” in robotic tasks. In this paper, we propose new model that can disentangle multiple dynamic factors. Since our method does not require modeling priors, it is capable of disentangling ”between” dynamic factors. In experiments, we show that FAVAE can extract the disentangled dynamic factors.
Learning Partially Observed PDE Dynamics with Neural Networks
Authors: Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Spatio-Temporal processes bear a central importance in many applied scientific fields. Generally, differential equations are used to describe these processes. In this work, we address the problem of learning spatio-temporal dynamics with neural networks when only partial information on the system's state is available. Taking inspiration from the dynamical system approach, we outline a general framework in which complex dynamics generated by families of differential equations  can be learned in a principled way. Two models are derived from this framework. We demonstrate how they can be applied in practice by considering the problem of forecasting fluid flows. We show how the underlying equations fit into our formalism and evaluate our method by comparing with standard baselines.
Weak contraction mapping and optimization
Authors: Siwei Luo
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Position

The weak contraction mapping is a self mapping that the range is always a subset of the domain, which admits a unique fixed-point. The iteration of weak contraction mapping is a Cauchy sequence that yields the unique fixed-point. A gradient-free optimization method as an application of weak contraction mapping is proposed to achieve global minimum convergence. The optimization method is robust to local minima and initial point position.
Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks
Authors: Jun Qi, Chin-Hui Lee, Javier Tejedor
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Machine Translation, Translation

The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs). However, high Tensor-Train ranks for all the core tensors of parameters need to be element-wise fixed, which results in an unnecessary redundancy of model parameters. This work applies Riemannian stochastic gradient descent (RSGD) to train core tensors of parameters in the Riemannian Manifold before finding vectors of lower Tensor-Train ranks for parameters. The paper first presents the RSGD algorithm with a convergence analysis and then tests it on more advanced Tensor-Train RNNs such as bi-directional GRU/LSTM and Encoder-Decoder RNNs with a Tensor-Train attention model. The experiments on digit recognition and machine translation tasks suggest the effectiveness of the RSGD algorithm for Tensor-Train RNNs.
Unseen Action Recognition with Unpaired Adversarial Multimodal Learning
Authors: AJ Piergiovanni, Michael S. Ryoo
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Action Recognition, General Classification, Temporal Action Localization

In this paper, we present a method to learn a joint multimodal representation space that allows for the recognition of unseen activities in videos. We compare the effect of placing various constraints on the embedding space using paired text and video data. Additionally, we propose a method to improve the joint embedding space using an adversarial formulation with unpaired text and video data. In addition to testing on publicly available datasets, we introduce a new, large-scale text/video dataset. We experimentally confirm that learning such shared embedding space benefits three difficult tasks (i) zero-shot activity classification, (ii) unsupervised activity discovery, and (iii) unseen activity captioning.
Unlabeled Disentangling of GANs with Guided Siamese Networks
Authors: Gökhan Yildirim, Nikolay Jetchev, Urs Bergmann
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Disentangling underlying generative factors of a data distribution is important for interpretability and generalizable representations. In this paper,  we introduce two novel disentangling methods. Our first method, Unlabeled Disentangling GAN (UD-GAN, unsupervised), decomposes the latent noise by generating similar/dissimilar image pairs and it learns a distance metric on these pairs with siamese networks and a contrastive loss. This pairwise approach provides consistent representations for similar data points. Our second method (UD-GAN-G, weakly supervised) modifies the UD-GAN with user-defined guidance functions, which restrict the information that goes into the siamese networks. This constraint helps UD-GAN-G to focus on the desired semantic variations in the data. We  show  that  both  our  methods  outperform  existing  unsupervised approaches in quantitative metrics that measure semantic accuracy of the learned representations. In addition, we illustrate that simple guidance functions we use in UD-GAN-G allow us to directly capture the desired variations in the data.
Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization
Authors: Wonwoong Cho, Seunghwan Choi, Junwoo Park, David Keetae Park, Tao Qin, Jaegul Choo
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image-to-Image Translation, Translation

Recently, image-to-image translation has seen a significant success. Among many approaches, image translation based on an exemplar image, which contains the target style information, has been popular, owing to its capability to handle multimodality as well as its suitability for practical use. However, most of the existing methods extract the style information from an entire exemplar and apply it to the entire input image, which introduces excessive image translation in irrelevant image regions. In response, this paper proposes a novel approach that jointly extracts out the local masks of the input image and the exemplar as targeted regions to be involved for image translation. In particular, the main novelty of our model lies in (1) co-segmentation networks for local mask generation and (2) the local mask-based highway adaptive instance normalization technique. We demonstrate the quantitative and the qualitative evaluation results to show the advantages of our proposed approach. Finally, the code is available at https://github.com/AnonymousIclrAuthor/Highway-Adaptive-Instance-Normalization
Empirically Characterizing Overparameterization Impact on Convergence
Authors: Newsha Ardalani, Joel Hestness, Gregory Diamos
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

A long-held conventional wisdom states that larger models train more slowly when using gradient descent. This work challenges this widely-held belief, showing that larger models can potentially train faster despite the increasing computational requirements of each training step. In particular, we study the effect of network structure (depth and width) on halting time and show that larger models---wider models in particular---take fewer training steps to converge.

We design simple experiments to quantitatively characterize the effect of overparametrization on weight space traversal. Results show that halting time improves when growing model's width for three different applications, and the improvement comes from each factor: The distance from initialized weights to converged weights shrinks with a power-law-like relationship, the average step size grows with a power-law-like relationship, and gradient vectors become more aligned with each other during traversal.
Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets
Authors: Abhinav Jauhri, Brad Stocks, Jian Hui Li, Koichi Yamada, John Paul Shen
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

This paper focuses on the synthetic generation of human mobility data in urban areas. We present a novel and scalable application of Generative Adversarial Networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride sharing/hailing services from four major cities in the US to train our GANs model. Our model captures the spatial and temporal variability of the ride-request patterns observed for all four cities on any typical day and over any typical week. Previous works have succinctly characterized the spatial and temporal properties of human mobility data sets using the fractal dimensionality and the densification power law, respectively, which we utilize to validate our GANs-generated synthetic data sets. Such synthetic data sets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility and intelligent transportation.
Traditional and Heavy Tailed Self Regularization in Neural Network Models
Authors: Charles H. Martin, Michael W. Mahoney
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet.  Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints.  Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify 5+1 Phases of Training, corresponding to increasing amounts of Implicit Self-Regularization.  For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a "size scale" separating signal from noise.  For state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed Self-Regularization, similar to the self-organization seen in the statistical physics of disordered systems.  This implicit Self-Regularization can depend strongly on the many knobs of the training process.  By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.
ISA-VAE: Independent Subspace Analysis with Variational Autoencoders
Authors: Jan Stühmer, Richard Turner, Sebastian Nowozin
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Disentanglement, Variational Inference

Recent work has shown increased interest in using the Variational Autoencoder (VAE) framework to discover interpretable representations of data in an unsupervised way. These methods have focussed largely on modifying the variational cost function to achieve this goal. However, we show that methods like beta-VAE simplify the tendency of variational inference to underfit causing pathological over-pruning and over-orthogonalization of learned components. In this paper we take a complementary approach: to modify the probabilistic model to encourage structured latent variable representations to be discovered. Specifically, the standard VAE probabilistic model is unidentifiable: the likelihood of the parameters is invariant under rotations of the latent space. This means there is no pressure to identify each true factor of variation with a latent variable.
We therefore employ a rich prior distribution, akin to the ICA model, that breaks the rotational symmetry.
Extensive quantitative and qualitative experiments demonstrate that the proposed prior mitigates the trade-off introduced by modified cost functions like beta-VAE and TCVAE between reconstruction loss and disentanglement. The proposed prior allows to improve these approaches with respect to both disentanglement and reconstruction quality significantly over the state of the art.
VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning
Authors: Hao Zhang, Bo Chen, Long Tian, Zhengjue Wang, Mingyuan Zhou
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Generation, Retrieval, Text Generation, Variational Inference, Zero-Shot Learning

To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network. To further improve VHE and add an image generator, we propose VHE randomized generative adversarial net (VHEGAN) that exploits the synergy between VHE and GAN through their shared latent space. After training with a hybrid stochastic-gradient MCMC/variational inference/stochastic gradient descent inference algorithm, VHEGAN can be used in a variety of settings, such as text generation/retrieval conditioning on an image, image generation/retrieval conditioning on a document/image, and generation of text-image pairs. The efficacy of VHEGAN is demonstrated quantitatively with experiments on both conventional and generalized ZSL tasks, and qualitatively on (conditional) image and/or text generation/retrieval.
$A^*$ sampling with probability matching
Authors: Yichi Zhou, Jun Zhu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decision Making

Probabilistic methods often need to draw samples from a nontrivial distribution. $A^*$ sampling is a nice algorithm by building upon a top-down construction of a Gumbel process, where a large state space is divided into subsets and at each round $A^*$ sampling selects a subset to process. However, the selection rule depends on a bound function, which can be intractable. Moreover, we show that such a selection criterion can be inefficient. This paper aims to improve $A^*$ sampling by addressing these issues. To design a suitable selection rule, we apply \emph{Probability Matching}, a widely used method for decision making, to $A^*$ sampling. We provide insights into the relationship between $A^*$ sampling and probability matching by analyzing a nontrivial special case in which the state space is partitioned into two subsets. We show that in this case probability matching is optimal within a constant gap. Furthermore, as directly applying probability matching to $A^*$ sampling is time consuming, we design an approximate version based on Monte-Carlo estimators. We also present an efficient implementation by leveraging special properties of Gumbel distributions and well-designed balanced trees. Empirical results show that our method saves a significantly amount of computational resources on suboptimal regions compared with $A^*$ sampling.
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN
Authors: Fangzhou Luo, Xiaolin Wu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deblurring, Denoising, Density Estimation, Generative Adversarial Network, Image Restoration, Super-Resolution

We propose a general method for various image restoration problems, such as denoising, deblurring, super-resolution and inpainting. The problem is formulated as a constrained optimization problem. Its objective is to maximize a posteriori probability of latent variables, and its constraint is that the image generated by these latent variables must be the same as the degraded image. We use a Generative Adversarial Network (GAN) as our density estimation model. Convincing results are obtained on MNIST dataset.
Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection
Authors: Hwanjun Song, Sundong Kim, Minseok Kim, Jae-Gil Lee
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Neural networks can converge faster with help from a smarter batch selection strategy. In this regard, we propose Ada-Boundary, a novel adaptive-batch selection algorithm that constructs an effective mini-batch according to the learning progress of the model.Our key idea is to present confusing samples what the true label is. Thus, the samples near the current decision boundary are considered as the most effective to expedite convergence. Taking advantage of our design, Ada-Boundary maintains its dominance in various degrees of training difficulty. We demonstrate the advantage of Ada-Boundary by extensive experiments using two convolutional neural networks for three benchmark data sets. The experiment results show that Ada-Boundary improves the training time by up to 31.7% compared with the state-of-the-art strategy and by up to 33.5% compared with the baseline strategy.
Neural Network Cost Landscapes as Quantum States
Authors: Abdulah Fawaz, Sebastien Piat, Paul Klein, Peter Mountney, Simone Severini
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Quantum computers promise significant advantages over classical computers for a number of different applications. We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. We demonstrate this explicitly for a binary neural network and, further, show how a quantum computer can train the network by manipulating this state using a well-known algorithm known as quantum amplitude amplification. We further show that with minor adaptation, this method can also represent the meta-loss landscape of a number of neural network architectures simultaneously. We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.
Over-parameterization Improves Generalization in the XOR Detection Problem
Authors: Alon Brutzkus, Amir Globerson
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization. However, there is currently no theoretical analysis that explains this observation. In this work, we study a simplified learning task with over-parameterized convolutional networks that empirically exhibits the same qualitative phenomenon.  For this setting, we provide a theoretical analysis of the optimization and generalization performance of gradient descent. Specifically, we prove data-dependent sample complexity bounds which show that over-parameterization improves the generalization performance of gradient descent.
On the effect of the activation function on the distribution of hidden nodes in a deep network
Authors: Philip M. Long and Hanie Sedghi
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We analyze the joint probability distribution on the lengths of the
vectors of hidden variables in different layers of a fully connected
deep network, when the weights and biases are chosen randomly according to
Gaussian distributions, and the input is binary-valued.  We show
that, if the activation function satisfies a minimal set of
assumptions, satisfied by all activation functions that we know that
are used in practice, then, as the width of the network gets large,
the ``length process'' converges in probability to a length map
that is determined as a simple function of the variances of the
random weights and biases, and the activation function.

We also show that this convergence may fail for activation functions 
that violate our assumptions.