pwc-archive/papers-with-abstracts huggingface dataset:
Selectivity metrics can overestimate the selectivity of units: a case study on AlexNet
Authors: Ella M. Gale, Anh Nguyen, Ryan Blything, Nicholas Martin and Jeffrey S. Bowers
Date: 2018-09-27 00:00:00

Various methods of measuring unit selectivity have been developed in order to understand the representations learned by neural networks (NNs).  Here we undertake a comparison of four such measures on AlexNet, namely, localist selectivity, \precision (Zhou et al, ICLR 2015), class-conditional mean activity selectivity CCMAS; (Morcos et al, ICLR 2018), and a new measure called top-class selectivity. In contrast with previous work on recurrent neural networks (RNNs), we fail to find any 100\% selective `localist units' in AlexNet, and demonstrate that the \precision and CCMAS measures provide a much higher level of selectivity than is warranted, with the most selective hidden units only responding strongly to a small minority of images from within a category. We also generated activation maximization (AM) images that maximally activated individual units and found that under (5\%) of units in fc6 and conv5 produced interpretable images of objects, whereas fc8 produced over 50\% interpretable images. Furthermore, the interpretable images in the hidden layers were not associated with highly selective units. These findings highlight the problem with current selectivity measures and show that new measures are required in order to provide a better assessment of learned representations in NNs.  We also consider why localist representations are learned in RNNs and not AlexNet.
Transferrable End-to-End Learning for Protein Interface Prediction
Authors: Raphael J. L. Townshend, Rishi Bedi, Ron O. Dror
Date: 2018-09-27 00:00:00
Tasks: Protein Interface Prediction, Transfer Learning

While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question.  In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability.  To do so, we focus on a central problem in biology: predicting how proteins interact with one another—that is, which surfaces of one protein bind to which surfaces of another protein.  We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction.  Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on hand-engineered, high-level features.  These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another.  Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.
Visual Imitation Learning with Recurrent Siamese Networks
Authors: Glen Berseth, Christopher J. Pal
Date: 2018-09-27 00:00:00
Tasks: Imitation Learning

People are incredibly skilled at imitating others by simply observing them. They achieve this even in the presence of significant morphological differences and capabilities. Further, people are able to do this from raw perceptions of the actions of others, without direct access to the abstracted demonstration actions and with only partial state information. People therefore solve a difficult problem of understanding the salient features of both observations of others and the relationship to their own state when learning to imitate specific tasks.
However, we can attempt to reproduce a similar demonstration via trail and error and through this gain more understanding of the task space.
To reproduce this ability an agent would need to both learn how to recognize the differences between itself and some demonstration and at the same time learn to minimize the distance between its own performance and that of the demonstration.
In this paper we propose an approach using only visual information to learn a distance metric between agent behaviour and a given video demonstration.
We train an RNN-based siamese model to compute distances in space and time between motion clips while training an RL policy to minimize this distance.
Furthermore, we examine a particularly challenging form of this problem where the agent must learn an imitation based task given a single demonstration.
We demonstrate our approach in the setting of deep learning based control for physical simulation of humanoid walking in both 2D with $10$ degrees of freedom (DoF) and 3D with $38$ DoF.
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
Authors: Artur Souza, Leonardo B. Oliveira, Sabine Hollatz, Matt Feldman, Kunle Olukotun, James M. Holton, Aina E. Cohen, Luigi Nardi
Date: 2018-09-27 00:00:00
Tasks: AutoML, Classification

Serial crystallography is the field of science that studies the structure and properties of crystals via diffraction patterns. In this paper, we introduce a new serial crystallography dataset generated through the use of a simulator; the synthetic images are labeled and they are both scalable and accurate. The resulting synthetic dataset is called DiffraNet, and it is composed of 25,000 512x512 grayscale labeled images. We explore several computer vision approaches for classification on DiffraNet such as standard feature extraction algorithms associated with Random Forests and Support Vector Machines but also an end-to-end CNN topology dubbed DeepFreak tailored to work on this new dataset. All implementations are publicly available and have been fine-tuned using off-the-shelf AutoML optimization tools for a fair comparison. Our best model achieves 98.5% accuracy. We believe that the DiffraNet dataset and its classification methods will have in the long term a positive impact in accelerating discoveries in many disciplines, including chemistry, geology, biology, materials science, metallurgy, and physics.
Exploring the interpretability of LSTM neural networks over multi-variable data
Authors: Tian Guo, Tao Lin
Date: 2018-09-27 00:00:00
Tasks: Time Series, Time Series Analysis

In learning a predictive model over multivariate time series consisting of target and exogenous variables, the forecasting performance and interpretability of the model are both essential for deployment and uncovering knowledge behind the data.
To this end, we propose the interpretable multi-variable LSTM recurrent neural network (IMV-LSTM) capable of providing accurate forecasting as well as both temporal and variable level importance interpretation.
In particular, IMV-LSTM is equipped with tensorized hidden states and update process, so as to learn variables-wise hidden states. 
On top of it, we develop a mixture attention mechanism and associated summarization methods to quantify the temporal and variable importance in data. 
Extensive experiments using real datasets demonstrate the prediction performance and interpretability of IMV-LSTM in comparison to a variety of baselines. 
It also exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variate data.
An Energy-Based Framework for Arbitrary Label Noise Correction
Authors: Jaspreet Sahota, Divya Shanmugam, Janahan Ramanan, Sepehr Eghbali, Marcus Brubaker
Date: 2018-09-27 00:00:00
Tasks: Binary Classification

We propose an energy-based framework for correcting mislabelled training examples in the context of binary classification. While existing work addresses random and class-dependent label noise, we focus on feature dependent label noise, which is ubiquitous in real-world data and difficult to model. Two elements distinguish our approach from others: 1) instead of relying on the original feature space, we employ an autoencoder to learn a discriminative representation and 2) we introduce an energy-based formalism for the label correction problem. We prove that a discriminative representation can be learned by training a generative model using a loss function comprised of the difference of energies corresponding to each class. The learned energy value for each training instance is compared to the original training labels and contradictions between energy assignment and training label are used to correct labels. We validate our method across eight datasets, spanning synthetic and realistic settings, and demonstrate the technique's state-of-the-art label correction performance. Furthermore, we derive analytical expressions to show the effect of label noise on the gradients of empirical risk.
End-to-End Multi-Lingual Multi-Speaker Speech Recognition
Authors: Hiroshi Seki, Takaaki Hori, Shinji Watanabe, Jonathan Le Roux, John R. Hershey
Date: 2018-09-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The expressive power of end-to-end automatic speech recognition (ASR) systems enables direct estimation of the character or word label sequence from  a sequence of acoustic features. Direct optimization of the whole system is advantageous because it not only eliminates the internal linkage necessary for hybrid systems,  but also extends the scope of potential application use cases by training the model for multiple objectives. Several multi-lingual ASR systems were recently proposed based on a monolithic neural network architecture without language-dependent modules, showing that modeling of multiple languages is well within the capabilities of an end-to-end framework. There has also been growing interest in multi-speaker speech recognition, which enables generation of multiple label sequences from single-channel mixed speech. In particular, a multi-speaker end-to-end ASR system that can directly model one-to-many mappings without additional auxiliary clues was recently proposed. In this paper, we propose an all-in-one end-to-end multi-lingual multi-speaker ASR system that integrates the capabilities of these two systems.  The proposed model is evaluated using mixtures of two speakers generated by using 10 languages, including mixed-language utterances.
Policy Generalization In Capacity-Limited Reinforcement Learning
Authors: Rachel A. Lerch, Chris R. Sims
Date: 2018-09-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Motivated by the study of generalization in biological intelligence, we examine reinforcement learning (RL) in settings where there are information-theoretic constraints placed on the learner's ability to represent a behavioral policy. We first show that the problem of optimizing expected utility within capacity-limited learning agents maps naturally to the mathematical field of rate-distortion (RD) theory. Applying the RD framework to the RL setting, we develop a new online RL algorithm, Capacity-Limited Actor-Critic, that learns a policy that optimizes a tradeoff between utility maximization and information processing costs. Using this algorithm in a 2D gridworld environment, we demonstrate two novel empirical results. First, at high information rates (high channel capacity), the algorithm achieves faster learning and discovers better policies compared to the standard tabular actor-critic algorithm. Second, we demonstrate that agents with capacity-limited policy representations avoid 'overfitting' and exhibit superior transfer to modified environments, compared to policies learned by agents with unlimited information processing resources. Our work provides a principled framework for the development of computationally rational RL agents.
Learning Neural Random Fields with Inclusive Auxiliary Generators
Authors: Yunfu Song, Zhijian Ou
Date: 2018-09-27 00:00:00
Tasks: Image Generation

Neural random fields (NRFs), which are defined by using neural networks to implement potential functions in undirected models, provide an interesting family of model spaces for machine learning. In this paper we develop a new approach to learning NRFs with inclusive-divergence minimized auxiliary generator - the inclusive-NRF approach, for continuous data (e.g. images), with solid theoretical examination on exploiting gradient information in model sampling. We show that inclusive-NRFs can be flexibly used in unsupervised/supervised image generation and semi-supervised classification, and empirically to the best of our knowledge, represent the best-performed random fields in these tasks. Particularly, inclusive-NRFs achieve state-of-the-art sample generation quality on CIFAR-10 in both unsupervised and supervised settings. Semi-supervised inclusive-NRFs show strong classification results on par with state-of-the-art generative model based semi-supervised learning methods, and simultaneously achieve superior generation, on the widely benchmarked datasets - MNIST, SVHN and CIFAR-10.
Recycling the discriminator for improving the inference mapping of GAN
Authors: Duhyeon Bang, Hyunjung Shim
Date: 2018-09-27 00:00:00

Generative adversarial networks (GANs) have achieved outstanding success in generating the high-quality data. Focusing on the generation process, existing GANs learn a unidirectional mapping from the latent vector to the data. Later, various studies point out that the latent space of GANs is semantically meaningful and can be utilized in advanced data analysis and manipulation. In order to analyze the real data in the latent space of GANs, it is necessary to investigate the inverse generation mapping from the data to the latent vector. To tackle this problem, the bidirectional generative models introduce an encoder to establish the inverse path of the generation process. Unfortunately, this effort leads to the degradation of generation quality because the imperfect generator rather interferes the encoder training and vice versa. 
In this paper, we propose an effective algorithm to infer the latent vector based on existing unidirectional GANs by preserving their generation quality.
It is important to note that we focus on increasing the accuracy and efficiency of the inference mapping but not influencing the GAN performance (i.e., the quality or the diversity of the generated sample).
Furthermore, utilizing the proposed inference mapping algorithm, we suggest a new metric for evaluating the GAN models by measuring the reconstruction error of unseen real data.
The experimental analysis demonstrates that the proposed algorithm achieves more accurate inference mapping than the existing method and provides the robust metric for evaluating GAN performance.
On Difficulties of Probability Distillation
Authors: Chin-wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville
Date: 2018-09-27 00:00:00

Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications. We identify a pathological optimization issue with the commonly adopted stochastic minimization of the (reverse) KL divergence, owing to sparse gradient signal from the teacher model due to curse of dimensionality. We also explore alternative principles for distillation, and show that one can achieve qualitatively better results than with KL minimization.
AIM: Adversarial Inference by Matching Priors and Conditionals
Authors: Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao
Date: 2018-09-27 00:00:00

Effective inference for a generative adversarial model remains an important and challenging problem. We propose a novel approach, Adversarial Inference by Matching priors and conditionals (AIM), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model. We derive an equivalent form of the prior and conditional matching objective that can be optimized efficiently without any parametric assumption on the data. We validate the effectiveness of AIM on the MNIST, CIFAR-10, and CelebA datasets by conducting quantitative and qualitative evaluations. Results demonstrate that AIM significantly improves both reconstruction and generation as compared to other adversarial inference models.
Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries
Authors: Felix Berkenkamp, Debadeepta Dey, Ashish Kapoor
Date: 2018-09-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has enabled robots to complete complex tasks in simulation. However, the resulting policies do not transfer to real robots due to model errors in the simulator. One solution is to randomize the simulation environment, so that the resulting, trained policy achieves high performance in expectation over a variety of configurations that could represent the real-world. However, the distribution over simulator configurations must be carefully selected to represent the relevant dynamic modes of the system, as otherwise it can be unlikely to sample challenging configurations frequently enough. Moreover, the ideal distribution to improve the policy changes as the policy (un)learns to solve tasks in certain configurations. In this paper, we propose to use an inexpensive, kernel-based summarization method method that identifies configurations that lead to diverse behaviors. Since failure modes for the given task are naturally diverse, the policy trains on a mixture of representative and challenging configurations, which leads to more robust policies. In experiments, we show that the proposed method achieves the same performance as domain randomization in simple cases, but performs better when domain randomization does not lead to diverse dynamic modes.
Gradient Acceleration in Activation Functions
Authors: Sangchul Hahn, Heeyoul Choi
Date: 2018-09-27 00:00:00

Dropout has been one of standard approaches to train deep neural networks, and it is known to regularize large models to avoid overfitting. The effect of dropout has been explained by avoiding co-adaptation.
In this paper, however, we propose a new explanation of why dropout works and propose a new technique to design better activation functions. First, we show that dropout can be explained as an optimization technique to push the input towards the saturation area of nonlinear activation function by accelerating gradient information flowing even in the saturation area in backpropagation. Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area. Then, input to the activation function can climb onto the saturation area which makes the network more robust because the model converges on a flat region.  
Experiment results support our explanation of dropout and confirm that the proposed GAAF technique improves performances with expected properties.
COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
Authors: Arbaaz Khan, Clark Zhang, Vijay Kumar, Alejandro Ribeiro
Date: 2018-09-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A deep reinforcement learning solution is developed for a collaborative multiagent system. Individual agents choose actions in response to the state of the environment, their own state, and possibly partial information about the state of other agents. Actions are chosen to maximize a collaborative long term discounted reward that encompasses the individual rewards collected by each agent. The paper focuses on developing a scalable approach that applies to large swarms of homogeneous agents. This is accomplished by forcing the policies of all agents to be the same resulting in a constrained formulation in which the experiences of each agent inform the learning process of the whole team, thereby enhancing the sample efficiency of the learning process. A projected coordinate policy gradient descent algorithm is derived to solve the constrained reinforcement learning problem. Experimental evaluations in collaborative navigation, a multi-predator-multi-prey game, and a multiagent survival game show marked improvements relative to methods that do not exploit the policy equivalence that naturally arises in homogeneous swarms.
Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourages convex latent distributions
Authors: Tim Sainburg, Marvin Thielk, Brad Thielman, Benjamin Migliori, Timothy Gentner
Date: 2018-09-27 00:00:00
Tasks: Generative Adversarial Network

We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations. By using an AE as both the generator and discriminator of a GAN, we pass a pixel-wise error function across the discriminator, yielding an AE which produces sharp samples that match both high- and low-level features of the original images. Samples generated from interpolations between data in latent space remain within the distribution of real data as trained by the discriminator, and therefore preserve realistic resemblances to the network inputs.
Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-valued Inverse Reinforcement Learning
Authors: Baoxiang Wang, Tongfang Sun, Xianjun Sam Zheng
Date: 2018-09-27 00:00:00

In recent years, reinforcement learning methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go and Poker.
However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, which are essential for understanding the agents' diverse behavior.
In this paper, we present a multi-motivation behavior modeling which investigates the multifaceted human motivations and models the underlying value structure of the agents.
Our approach extends inverse RL to the vectored-valued setting which imposes a much weaker assumption than previous studies.
The vectorized rewards incorporate Pareto optimality, which is a powerful tool to explain a wide range of behavior by its optimality.
For practical assessment, our algorithm is tested on the World of Warcraft Avatar History dataset spanning three years of the gameplay.
Our experiments demonstrate the improvement over the scalarization-based methods on real-world problem settings.
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression
Authors: Wenyuan Zeng, Raquel Urtasun
Date: 2018-09-27 00:00:00
Tasks: Model Compression, Neural Network Compression

Model compression can significantly reduce the computation and memory footprint of large neural networks. To achieve a good trade-off between model size and accuracy, popular compression techniques usually rely on hand-crafted heuristics and
require manually setting the compression ratio of each layer. This process is typically costly and suboptimal. In this paper, we propose a Multi-Layer Pruning method (MLPrune), which is theoretically sound, and can automatically decide appropriate compression ratios for all layers. Towards this goal, we use an efficient approximation of the Hessian as our pruning criterion, based on a Kronecker-factored Approximate Curvature method. We demonstrate the effectiveness of our method on several datasets and architectures, outperforming previous state-of-the-art by a large margin. Our experiments show that we can compress AlexNet and VGG16 by 25x without loss in accuracy on ImageNet. Furthermore, our method has much fewer hyper-parameters and requires no expert knowledge.