pwc-archive/papers-with-abstracts huggingface dataset:
Towards Provable Control for Unknown Linear Dynamical Systems
Authors: Sanjeev Arora, Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state. Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control in this setting as a convex program. This approach eliminates the need to solve the non-convex problem of explicit identification of the system and its latent state, and allows for provable optimality guarantees for the control signal. We give the first efficient algorithm for finding the optimal control signal with an arbitrary time horizon T, with sample complexity (number of training rollouts) polynomial only in log(T) and other relevant parameters.
Depth separation and weight-width trade-offs for sigmoidal neural networks
Authors: Amit Deshpande, Navin Goyal, Sushrut Karmalkar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Some recent work has shown separation between the expressive power of depth-2 and depth-3 neural networks. These separation results are shown by constructing functions and input distributions, so that the function is well-approximable by a depth-3 neural network of polynomial size but it cannot be well-approximated under the chosen input distribution by any depth-2 neural network of polynomial size. These results are not robust and require carefully chosen functions as well as input distributions.

We show a similar separation between the expressive power of depth-2 and depth-3 sigmoidal neural networks over a large class of input distributions, as long as the weights are polynomially bounded. While doing so, we also show that depth-2 sigmoidal neural networks with small width and small weights can be well-approximated by low-degree multivariate polynomials.
Discovering the mechanics of hidden neurons
Authors: Simon Carbonnelle, Christophe De Vleeschouwer
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding. This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons. While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown. We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing. During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training. During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction. These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments.
DNN Model Compression Under Accuracy Constraints
Authors: Soroosh Khoram, Jing Li
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: model, Model Compression

The growing interest to implement Deep Neural Networks (DNNs) on resource-bound hardware has motivated innovation of compression algorithms. Using these algorithms, DNN model sizes can be substantially reduced, with little to no accuracy degradation. This is achieved by either eliminating components from the model, or penalizing complexity during training. While both approaches demonstrate considerable compressions, the former often ignores the loss function during compression while the later produces unpredictable compressions. In this paper, we propose a technique that directly minimizes both the model complexity and the changes in the loss function. In this technique, we formulate compression as a constrained optimization problem, and then present a solution for it. We will show that using this technique, we can achieve competitive results.
Using Deep Reinforcement Learning to Generate Rationales for Molecules
Authors: Benson Chen, Connor Coley, Regina Barzilay, Tommi Jaakkola
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Drug Design, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning algorithms are increasingly used in modeling chemical processes. However, black box predictions without rationales have limited used in practical applications, such as drug design. To this end, we learn to identify molecular substructures -- rationales -- that are associated with the target chemical property (e.g., toxicity). The rationales are learned in an unsupervised fashion, requiring no additional information beyond the end-to-end task. We formulate this problem as a reinforcement learning problem over the molecular graph, parametrized by two convolution networks corresponding to the rationale selection and prediction based on it, where the latter induces the reward function. We evaluate the approach on two benchmark toxicity datasets. We demonstrate that our model sustains high performance under the additional constraint that predictions strictly follow the rationales. Additionally, we validate the extracted rationales through comparison against those described in chemical literature and through synthetic experiments.
Fast Node Embeddings: Learning Ego-Centric Representations
Authors: Tiago Pimentel, Adriano Veloso, Nivio Ziviani
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Link Prediction, Machine Translation, Node Classification, Question Answering, Representation Learning, speech-recognition, Speech Recognition, Translation

Representation learning is one of the foundations of Deep Learning and allowed important improvements on several Machine Learning tasks, such as Neural Machine Translation, Question Answering and Speech Recognition. Recent works have proposed new methods for learning representations for nodes and edges in graphs. Several of these methods are based on the SkipGram algorithm, and they usually process a large number of multi-hop neighbors in order to produce the context from which node representations are learned. In this paper, we propose an effective and also efficient method for generating node embeddings in graphs that employs a restricted number of permutations over the immediate neighborhood of a node as context to generate its representation, thus ego-centric representations. We present a thorough evaluation showing that our method outperforms state-of-the-art methods in six different datasets related to the problems of link prediction and node classification, being one to three orders of magnitude faster than baselines when generating node embeddings for very large graphs.
HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
Authors: Yanqi Zhou, Wei Ping, Sercan Arik, Kainan Peng, Greg Diamos
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Speech Synthesis, text-to-speech, Text to Speech

This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive
models for raw audio waveform generation. As an example, we propose
a hybrid model that combines an autoregressive network named WaveNet and a
conventional LSTM model to address speech synthesis. Instead of generating
one sample per time-step, the proposed HybridNet generates multiple samples per
time-step by exploiting the long-term memory utilization property of LSTMs. In
the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.
HybridNet achieves a 3.83 subjective 5-scale mean opinion score on
US English, largely outperforming the same size WaveNet in terms of naturalness
and provide 2x speed up at inference.
Sparse Regularized Deep Neural Networks For Efficient Embedded Learning
Authors: Jia Bi
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Deep learning is becoming more widespread in its application due to its power in solving complex classification problems. However, deep learning models often require large memory and energy consumption, which may prevent them from being deployed effectively on embedded platforms, limiting their applications. This work addresses the problem by proposing methods {\em Weight Reduction Quantisation} for compressing the memory footprint of the models, including reducing the number of weights and the number of bits to store each weight. Beside, applying with sparsity-inducing regularization, our work focuses on speeding up stochastic variance reduced gradients (SVRG) optimization on non-convex problem. Our method that mini-batch SVRG with $\ell$1 regularization on non-convex problem has faster and smoother convergence rates than SGD by using adaptive learning rates. Experimental evaluation of our approach uses MNIST and CIFAR-10 datasets on LeNet-300-100 and LeNet-5 models, showing our approach can reduce the memory requirements both in the convolutional and fully connected layers by up to 60$\times$ without affecting their test accuracy.
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Causal Inference, counterfactual

Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods.
Convergence rate of sign stochastic gradient descent for non-convex functions
Authors: Jeremy Bernstein, Kamyar Azizzadenesheli, Yu-Xiang Wang, Anima Anandkumar
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Distributed Optimization, Quantization

The sign stochastic gradient descent method (signSGD) utilizes only the sign of the stochastic gradient in its updates. Since signSGD carries out one-bit quantization of the gradients, it is extremely practical for distributed optimization where gradients need to be aggregated from different processors. For the first time, we establish convergence rates for signSGD on general non-convex functions under transparent conditions. We show that the rate of signSGD to reach first-order critical points matches that of SGD in terms of number of stochastic gradient calls, up to roughly a linear factor in the dimension. We carry out simple experiments to explore the behaviour of sign gradient descent (without the stochasticity) close to saddle points and show that it often helps completely avoid them without using either stochasticity or curvature information.
CNNs as Inverse Problem Solvers and Double Network Superresolution
Authors: Cem Tarhan, Gözde BOZDAĞI AKAR
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. We show how a single neuron is able to provide the optimum solution for inverse problem, given a low resolution image dictionary as an operator. Introducing a new concept called Representation Dictionary Duality, we show that CNN elements (filters) are trained to be representation vectors and then, during reconstruction, used as dictionaries. In the light of theoretical work, we propose a new algorithm which uses two networks with different structures that are separately trained with low and high coherency image patches and show that it performs faster compared to the state-of-the-art algorithms while not sacrificing from performance.
How do deep convolutional neural networks learn from raw audio waveforms?
Authors: Yuan Gong, Christian Poellabauer
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Prior work on speech and audio processing has demonstrated the ability to obtain excellent performance when learning directly from raw audio waveforms using convolutional neural networks (CNNs). However, the exact inner workings of a CNN remain unclear, which hinders further developments and improvements into this direction. In this paper, we theoretically analyze and explain how deep CNNs learn from raw audio waveforms and identify potential limitations of existing network structures. Based on this analysis, we further propose a new network architecture (called SimpleNet), which offers a very simple but concise structure and high model interpretability.
Evaluation of generative networks through their data augmentation capacity
Authors: Timothée Lesort, Florian Bordes, Jean-Francois Goudou, David Filliat
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation

Generative networks are known to be difficult to assess. Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images. But the validation of their quality is highly dependent on the method used. A good generator should generate data which contain meaningful and varied information and that fit the distribution of a dataset. This paper presents a new method to assess a generator. Our approach is based on training a classifier with a mixture of real and generated samples. We train a generative model over a labeled training set, then we use this generative model to sample new data points that we mix with the original training data. This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset. We compare this result with the score of the same classifier trained on the real training data mixed with noise. By computing the classifier's accuracy with different ratios of samples from both distributions (real and generated) we are able to estimate if the generator successfully fits and is able to generalize the distribution of the dataset. Our experiments compare the result of different generators from the VAE and GAN framework on MNIST and fashion MNIST dataset.
On the Generalization Effects of DenseNet Model Structures
Authors: Yin Liu, Vincent Chen
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation, model

Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance. While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied. 
Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures' implicit regularization properties? 
In this work, we investigate the skip connection's effect on network's generalization features. Through experiments, we show that certain neural network architectures contribute to their generalization abilities. Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with 'skip connections'. We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.
INTERPRETATION OF NEURAL NETWORK IS FRAGILE
Authors: Amirata Ghorbani, Abubakar Abid, James Zou
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning, Feature Importance

In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predictions. For example, if an algorithm classifies a given pathology image to be a malignant tumor, then the doctor may need to know which parts of the image led the algorithm to this classification. How to interpret black-box predictors is thus an important and active area of research.  A fundamental question is: how much can we trust the interpretation itself? In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense:  two perceptively indistinguishable inputs with the same predicted label can be assigned very different}interpretations. We systematically characterize the fragility of the interpretations generated by several widely-used feature-importance interpretation methods (saliency maps, integrated gradient, and DeepLIFT) on ImageNet and CIFAR-10. Our experiments show that even small random perturbation can change the feature importance and new systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly fragile. Our analysis of the geometry of the Hessian matrix gives insight on why fragility could be a fundamental challenge to the current interpretation approaches.
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model
Authors: Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Neural Architecture Search

We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods. In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model. The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss. On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57.8, which is state-of-the-art among automatic model design methods on Penn Treebank. On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 2.89%, close to the 2.65% achieved by standard NAS (Zoph et al., 2017). Most importantly, our experiments show that ENAS is more than 10x faster and 100x less resource-demanding than NAS.
Deep Lipschitz networks and Dudley GANs
Authors: Ehsan Abbasnejad, Javen Shi, Anton Van Den Hengel
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Generative adversarial networks (GANs) have enjoyed great success, however often suffer instability during training which motivates many attempts to resolve this issue. Theoretical explanation for the cause of instability is provided in Wasserstein GAN (WGAN), and wasserstein distance is proposed to stablize the training. Though WGAN is indeed more stable than previous GANs, it takes much more iterations and time to train. This is because the ways to ensure Lipschitz condition in WGAN (such as weight-clipping) significantly limit the capacity of the network. In this paper, we argue that it is beneficial to ensure Lipschitz condition as well as maintain sufficient capacity and expressiveness of the network. To facilitate this, we develop both theoretical and practical building blocks, using which one can construct different neural networks using a large range of metrics, as well as ensure Lipschitz condition and sufficient capacity of the networks. Using the proposed building blocks, and a special choice of a metric called Dudley metric, we propose Dudley GAN that outperforms the state of the arts in both convergence and sample quality. We discover a natural link between Dudley GAN (and its extension) and empirical risk minimization, which gives rise to generalization analysis.
Feature Map Variational Auto-Encoders
Authors: Lars Maaløe, Ole Winther
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Generation

There have been multiple attempts with variational auto-encoders (VAE) to learn powerful global representations of complex data using a combination of latent stochastic variables and an autoregressive model over the dimensions of the data. However, for the most challenging natural image tasks the purely autoregressive model with stochastic variables still outperform the combined stochastic autoregressive models. In this paper, we present simple additions to the VAE framework that generalize to natural images by embedding spatial information in the stochastic layers. We significantly improve the state-of-the-art results on MNIST, OMNIGLOT, CIFAR10 and ImageNet when the feature map parameterization of the stochastic variables are combined with the autoregressive PixelCNN approach. Interestingly, we also observe close to state-of-the-art results without the autoregressive part. This opens the possibility for high quality image generation with only one forward-pass.
Structured Exploration via Hierarchical Variational Policy Networks
Authors: Stephan Zheng, Yisong Yue
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning, Variational Inference

Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample efficiency of on-policy exploration in large state-action spaces. The key idea is to model a stochastic policy as a hierarchical latent variable model, which can learn low-dimensional structure in the state-action space, and to define exploration by sampling from the low-dimensional latent space. This approach enables lower sample complexity, while preserving policy expressivity. In order to make learning tractable, we derive a joint learning and exploration strategy by combining hierarchical variational inference with actor-critic learning. The benefits of our learning approach are that 1) it is principled, 2) simple to implement, 3) easily scalable to settings with many actions and 4) easily composable with existing deep learning approaches. We demonstrate the effectiveness of our approach on learning a deep centralized multi-agent policy, as multi-agent environments naturally have an exponentially large state-action space. In this setting, the latent hierarchy implements a form of multi-agent coordination during exploration and execution (MACE). We demonstrate empirically that MACE can more efficiently learn optimal policies in challenging multi-agent games with a large number (~20) of agents, compared to conventional baselines. Moreover, we show that our hierarchical structure leads to meaningful agent coordination.