pwc-archive/papers-with-abstracts huggingface dataset:
Large Scale Optimal Transport and Mapping Estimation
Authors: Vivien Seguy, Bharath Bhushan Damodaran, Remi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Domain Adaptation

This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularized OT, and show empirically that it scales better than a recent related approach when the amount of samples is very large. Second, we estimate a Monge map as a deep neural network learned by approximating the barycentric projection of the previously-obtained OT plan. This parameterization allows generalization of the mapping outside the support of the input measure. We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT and Monge map between the underlying continuous measures. We showcase our proposed approach on two applications: domain adaptation and generative modeling.
Action-dependent Control Variates for Policy Optimization via Stein Identity
Authors: Hao Liu*, Yihao Feng*, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effectively reduce variance for policy gradient methods. Motivated by the Stein’s identity, our method extends the previous control variate methods used in REINFORCE and advantage actor-critic by introducing more flexible and general action-dependent baseline functions. Empirical studies show that our method essentially improves the sample efficiency of the state-of-the-art policy gradient approaches.
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
Authors: Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

It is well known that it is possible to construct "adversarial examples"
for neural networks: inputs which are misclassified by the network
yet indistinguishable from true data. We propose a simple
modification to standard neural network architectures, thermometer
encoding, which significantly increases the robustness of the network to
adversarial examples. We demonstrate this robustness with experiments
on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that
models with thermometer-encoded inputs consistently have higher accuracy
on adversarial examples, without decreasing generalization.
State-of-the-art accuracy under the strongest known white-box attack was 
increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10.
We explore the properties of these networks, providing evidence
that thermometer encodings help neural networks to
find more-non-linear decision boundaries.
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
Authors: Kangwook Lee, Hoon Kim, Changho Suh
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Gaze Estimation

Collecting a large dataset with high quality annotations is expensive and time-consuming. Recently, Shrivastava et al. (2017) propose Simulated+Unsupervised (S+U) learning: It first learns a mapping from synthetic data to real data, translates a large amount of labeled synthetic data to the ones that resemble real data, and then trains a learning model on the translated data. Bousmalis et al. (2017) propose a similar framework that jointly trains a translation mapping and a learning model. 
While these algorithms are shown to achieve the state-of-the-art performances on various tasks, it may have a room for improvement, as they do not fully leverage flexibility of data simulation process and consider only the forward (synthetic to real) mapping. While these algorithms are shown to achieve the state-of-the-art performances on various tasks, it may have a room for improvement, as it does not fully leverage flexibility of data simulation process and consider only the forward (synthetic to real) mapping. Inspired by this limitation, we propose a new S+U learning algorithm, which fully leverage the flexibility  of  data  simulators and bidirectional mappings between synthetic data and real data. We show that our approach achieves the improved performance on the gaze estimation task, outperforming (Shrivastava et al., 2017).
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks
Authors: Pan Zhou, Jiashi Feng
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Generalization Bounds

This work aims to provide  comprehensive landscape analysis of empirical risk in deep neural networks (DNNs), including the convergence behavior of its gradient, its stationary points and the empirical risk itself to their corresponding population counterparts, which reveals how various network parameters determine the convergence performance. In particular, for an $l$-layer linear neural network consisting of $\dm_i$ neurons in the $i$-th layer, we prove the gradient of its empirical risk  uniformly converges to the one of its population risk, at the rate of $\mathcal{O}(r^{2l} \sqrt{l\sqrt{\max_i \dm_i} s\log(d/l)/n})$. Here $d$ is the total weight dimension, $s$ is the number of nonzero entries  of all the  weights and the magnitude  of weights per layer is upper bounded by $r$. Moreover, we prove the one-to-one correspondence of the non-degenerate stationary points between the empirical and population risks and provide convergence guarantee for each pair. We also establish the uniform convergence of the empirical risk to its population counterpart and further derive the stability and  generalization bounds for the empirical risk. In addition, we  analyze these properties for deep \emph{nonlinear} neural networks with sigmoid activation functions. We prove  similar results for convergence behavior of their empirical risk gradients, non-degenerate stationary points as well as the empirical risk itself.

To our best knowledge, this work is the first one theoretically characterizing the uniform convergence of the gradient and stationary points of the empirical risk of DNN models, which benefits the theoretical understanding on  how  the neural network depth $l$, the layer width $\dm_i$, the network size $d$, the sparsity in weight and the parameter magnitude $r$ determine the neural network landscape.
Monotonic Chunkwise Attention
Authors: Chung-Cheng Chiu*, Colin Raffel*
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Document Summarization, speech-recognition, Speech Recognition

Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed. We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time. When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism. In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.
A Framework for the Quantitative Evaluation of Disentangled Representations
Authors: Cian Eastwood, Christopher K. I. Williams
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Recent AI research has emphasised the importance of learning disentangled representations of the explanatory factors  behind data. Despite the growing interest in models which can learn such representations, visual inspection remains the standard evaluation metric. While various desiderata have been implied in recent definitions, it is currently unclear what exactly makes one disentangled representation better than another. In this work we propose a framework for the quantitative evaluation of disentangled representations when the ground-truth latent structure is available. Three criteria are explicitly defined and quantified to elucidate the quality of learnt representations and thus compare models on an equal basis. To illustrate the appropriateness of the framework, we employ it to compare quantitatively the representations learned by recent state-of-the-art models.
Adaptive Dropout with Rademacher Complexity Regularization
Authors: Ke Zhai, Huan Wang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Document Classification

We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. The state-of-the-art deep learning algorithms impose dropout strategy to prevent feature co-adaptation. However, choosing the dropout rates remains an art of heuristics or relies on empirical grid-search over some hyperparameter space. In this work, we show the network Rademacher complexity is bounded by a function related to the dropout rate vectors and the weight coefficient matrices. Subsequently, we impose this bound as a regularizer and provide a theoretical justified way to trade-off between model complexity and representation power. Therefore, the dropout rates and the empirical loss are unified into the same objective function, which is then optimized using the block coordinate descent algorithm. We discover that the adaptively adjusted dropout rates converge to some interesting distributions that reveal meaningful patterns.Experiments on the task of image and document classification also show our method achieves better performance compared to the state-of the-art dropout algorithms.
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning
Authors: Justin Fu, Katie Luo, Sergey Levine
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decision Making, Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning provides a powerful and general framework for decision
making and control, but its application in practice is often hindered by the need
for extensive feature and reward engineering. Deep reinforcement learning methods
can remove the need for explicit engineering of policy or value features, but
still require a manually specified reward function. Inverse reinforcement learning
holds the promise of automatic reward acquisition, but has proven exceptionally
difficult to apply to large, high-dimensional problems with unknown dynamics. In
this work, we propose AIRL, a practical and scalable inverse reinforcement learning
algorithm based on an adversarial reward learning formulation that is competitive
with direct imitation learning algorithms. Additionally, we show that AIRL is
able to recover portable reward functions that are robust to changes in dynamics,
enabling us to learn policies even under significant variation in the environment
seen during training.
Variational Network Quantization
Authors: Jan Achterhold, Jan Mathias Koehler, Anke Schmeink, Tim Genewein
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Quantization, Variational Inference

In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).
Identifying Analogies Across Domains
Authors: Yedid Hoshen, Lior Wolf
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Translation

Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain. In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques. We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function. The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.
Kronecker-factored Curvature Approximations for Recurrent Neural Networks
Authors: James Martens, Jimmy Ba, Matt Johnson
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017).  It is based on an approximation to the Fisher information matrix (FIM) that makes assumptions about the particular structure of the network and the way it is parameterized. The original K-FAC method was applicable only to fully-connected networks, although it has been recently extended by Grosse & Martens (2016) to handle convolutional networks as well. In this work we extend the method to handle RNNs by introducing a novel approximation to the FIM for RNNs. This approximation works by modelling the covariance structure between the gradient contributions at different time-steps using a chain-structured linear Gaussian graphical model, summing the various cross-covariances, and computing the inverse in closed form. We demonstrate in experiments that our method significantly outperforms general purpose state-of-the-art optimizers like SGD with momentum and Adam on several challenging RNN training tasks.
Multi-Task Learning for Document Ranking and Query Suggestion
Authors: Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Document Ranking, Multi-Task Learning

We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker, and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommender tracks users' query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users' underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.
Boundary Seeking GANs
Authors: R. Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Scene Understanding, Text Generation

Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not work for discrete data. We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator. The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs). We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation.  In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning.
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Authors: Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Anomaly Detection, Density Estimation, Dimensionality Reduction, Unsupervised Anomaly Detection, Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly, Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly

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.
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
Authors: Sebastian Nowozin
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Computational Efficiency, Variational Inference

The importance-weighted autoencoder (IWAE) approach of Burda et al. defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on $K$ samples we show the bias to be of order O(1/K). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI),
a family of bias-reduced estimators reducing the bias to $O(K^{-(m+1)})$ for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.

Our implementation is available at https://github.com/Microsoft/jackknife-variational-inference
Wavelet Pooling for Convolutional Neural Networks
Authors: Travis Williams, Robert Li
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification

Convolutional Neural Networks continuously advance the progress of 2D and 3D image and object classification. The steadfast usage of this algorithm requires constant evaluation and upgrading of foundational concepts to maintain progress. Network regularization techniques typically focus on convolutional layer operations, while leaving pooling layer operations without suitable options. We introduce Wavelet Pooling as another alternative to traditional neighborhood pooling. This method decomposes features into a second level decomposition, and discards the first-level subbands to reduce feature dimensions. This method addresses the overfitting problem encountered by max pooling, while reducing features in a more structurally compact manner than pooling via neighborhood regions. Experimental results on four benchmark classification datasets demonstrate our proposed method outperforms or performs comparatively with methods like max, mean, mixed, and stochastic pooling.
Few-Shot Learning with Graph Neural Networks
Authors: Victor Garcia Satorras, Joan Bruna Estrach
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Active Learning, Few-Shot Learning, Graph Neural Network

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on ‘relational’ tasks.