pwc-archive/papers-with-abstracts huggingface dataset:
Understanding & Generalizing AlphaGo Zero
Authors: Ravichandra Addanki, Mohammad Alizadeh, Shaileshh Bojja Venkatakrishnan, Devavrat Shah, Qiaomin Xie, Zhi Xu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling, Sequential Decision Making

AlphaGo Zero (AGZ) introduced a new {\em tabula rasa} reinforcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game. This success naturally begs the question whether it is possible to develop similar high-performance reinforcement learning algorithms for generic sequential decision-making problems (beyond two-player games), using only the constraints of the environment as the ``rules.'' To address this challenge, we start by taking steps towards developing a formal understanding of AGZ.  AGZ includes two key innovations: (1) it learns a policy (represented as a neural network) using {\em supervised learning} with cross-entropy loss from samples generated via Monte-Carlo Tree Search (MCTS); (2) it uses {\em self-play} to learn without training data. 

We argue that the self-play in AGZ corresponds to learning a Nash equilibrium for the two-player game; and the supervised learning with MCTS is attempting to learn the policy corresponding to the Nash equilibrium, by establishing a novel bound on the difference between the expected return achieved by two policies in terms of the expected KL divergence (cross-entropy) of their induced distributions. To extend AGZ to generic sequential decision-making problems, we introduce a {\em robust MDP} framework, in which the agent and nature effectively play a zero-sum game: the agent aims to take actions to maximize reward while nature seeks state transitions, subject to the constraints of that environment, that minimize the agent's reward. For a challenging network scheduling domain, we find that AGZ within the robust MDP framework provides near-optimal performance, matching one of the best known scheduling policies that has taken the networking community three decades of intensive research to develop.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Authors: Naganand Yadati, Vikram Nitin, Madhav Nimishakavi, Prateek Yadav, Anand Louis, Partha Talukdar
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Link Prediction, Prediction

Link prediction in simple graphs is a fundamental problem in which new links between nodes are predicted based on the observed structure of the graph. However, in many real-world applications, there is a need to model relationships among nodes which go beyond pairwise associations. For example, in a chemical reaction, relationship among the reactants and products is inherently higher-order. Additionally, there is need to represent the direction from reactants to products. Hypergraphs provide a natural way to represent such complex higher-order relationships. Even though Graph Convolutional Networks (GCN) have recently emerged as a powerful deep learning-based approach for link prediction over simple graphs, their suitability for link prediction in hypergraphs is unexplored -- we fill this gap in this paper and propose Neural Hyperlink Predictor (NHP). NHP adapts GCNs for link prediction in hypergraphs. We propose two variants of NHP --NHP-U and NHP-D -- for link prediction over undirected and directed hypergraphs, respectively. To the best of our knowledge, NHP-D is the first method for link prediction over directed hypergraphs. Through extensive experiments on multiple real-world datasets, we show NHP's effectiveness.
PRUNING IN TRAINING: LEARNING AND RANKING SPARSE CONNECTIONS IN DEEP CONVOLUTIONAL NETWORKS
Authors: Yanwei Fu, Shun Zhang, Donghao Li, Xinwei Sun, xiangyang xue, Yuan YAO
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks. Different from existing works, our PiT framework employs the sparse penalties to train networks and thus help rank the importance of weights and filters. Our PiT algorithms can directly prune the network without any fine-tuning. The pruned networks can still achieve comparable performance to the original networks. In particular, we introduce the (Group) Lasso-type Penalty (L-P /GL-P), and (Group) Split LBI Penalty (S-P / GS-P) to regularize the networks, and a pruning strategy proposed  is used in help prune the network. We conduct the extensive experiments on MNIST, Cifar-10, and miniImageNet. The results validate the efficacy of our proposed methods. Remarkably, on MNIST dataset, our PiT framework can save 17.5% parameter size of LeNet-5, which achieves the 98.47% recognition accuracy.
Integral Pruning on Activations and Weights for Efficient Neural Networks
Authors: Qing Yang, Wei Wen, Zuoguan Wang, Yiran Chen, Hai Li
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Model Compression

With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment. This work aims to advance the compression beyond the weights to the activations of DNNs. We propose the Integral Pruning (IP) technique which integrates the activation pruning with the weight pruning. Through the learning on the different importance of neuron responses and connections, the generated network, namely IPnet, balances the sparsity between activations and weights and therefore further improves execution efficiency. The feasibility and effectiveness of IPnet are thoroughly evaluated through various network models with different activation functions and on different datasets. With <0.5% disturbance on the testing accuracy, IPnet saves 71.1% ~ 96.35% of computation cost, compared to the original dense models with up to 5.8x and 10x reductions in activation and weight numbers, respectively.
ACIQ: Analytical Clipping for Integer Quantization of neural networks
Authors: Ron Banner, Yury Nahshan, Elad Hoffer, Daniel Soudry
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Quantization

We analyze the trade-off between quantization noise and clipping distortion in low precision networks. We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping. By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping. For example, just by choosing the accurate clipping values, more than 40\% accuracy improvement is obtained for the quantization of VGG-16 to 4-bits of precision. Our results have many applications for the quantization of neural networks at both training and inference time.
PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION
Authors: Wei Gao, Yi Wei, Quanquan Li, Hongwei Qin, Wanli Ouyang, Junjie Yan
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Pose Estimation

In this paper, we propose an efficient framework to accelerate convolutional neural networks. We utilize two types of acceleration methods: pruning and hints. Pruning can reduce model size by removing channels of layers. Hints can improve the performance of student model by transferring knowledge from teacher model. We demonstrate that pruning and hints are complementary to each other. On one hand, hints can benefit pruning by maintaining similar feature representations. On the other hand, the model pruned from teacher networks is a good initialization for student model, which increases the transferability between two networks. Our approach performs pruning stage and hints stage iteratively to further improve the
performance. Furthermore, we propose an algorithm to reconstruct the parameters of hints layer and make the pruned model more suitable for hints. Experiments were conducted on various tasks including classification and pose estimation. Results on CIFAR-10, ImageNet and COCO demonstrate the generalization and superiority of our framework.
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks
Authors: Royson Lee, Nic Lane, Marko Stankovic, Sourav Bhattacharya
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Restoration, Image Super-Resolution, Super-Resolution

A successful application of convolutional architectures is to increase the resolution of single low-resolution images -- a image restoration task called super-resolution (SR). Naturally, SR is of value to resource constrained devices like mobile phones, electronic photograph frames and televisions to enhance image quality. However, SR demands perhaps the most extreme amounts of memory and compute operations of any mainstream vision task known today, preventing SR from being deployed to devices that require them. In this paper, we perform a early systematic study of system resource efficiency for SR, within the context of a variety of architectural and low-precision approaches originally developed for discriminative neural networks. We present a rich set of insights, representative SR architectures, and efficiency trade-offs; for example, the prioritization of ways to compress models to reach a specific memory and computation target and techniques to compact SR models so that they are suitable for DSPs and FPGAs. As a result of doing so, we manage to achieve better and comparable performance with previous models in the existing literature, highlighting the practicality of using existing efficiency techniques in SR tasks. Collectively, we believe these results provides the foundation for further research into the little explored area of resource efficiency for SR.
On the Margin Theory of Feedforward Neural Networks
Authors: Colin Wei, Jason Lee, Qiang Liu, Tengyu Ma
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Past works have shown that, somewhat surprisingly, over-parametrization can help generalization in neural networks. Towards explaining this phenomenon, we adopt a margin-based perspective. We establish: 1) for multi-layer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for deep networks. In the case of two-layer networks, an infinite-width neural network enjoys the best generalization guarantees. The typical infinite feature methods are kernel methods; we compare the neural net margin with that of kernel methods and construct natural instances where kernel methods have much weaker generalization guarantees. We validate this gap between the two approaches empirically. Finally, this infinite-neuron viewpoint is also fruitful for analyzing optimization. We show that a perturbed gradient flow on infinite-size networks finds a global optimizer in polynomial time.
The role of over-parametrization in generalization of neural networks
Authors: Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann Lecun, Nathan Srebro
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization. In this work we suggest a novel complexity measure based on unit-wise capacities resulting in a tighter generalization bound for two layer ReLU networks. Our capacity bound correlates with the behavior of test error with increasing network sizes (within the range reported in the experiments), and could partly explain the improvement in generalization with over-parametrization. We further present a matching lower bound for the Rademacher complexity that improves over previous capacity lower bounds for neural networks.
Cross-Task Knowledge Transfer for Visually-Grounded Navigation
Authors: Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Disentanglement, Embodied Question Answering, Question Answering, Reinforcement Learning, Transfer Learning, Visual Navigation

Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for two different tasks: learning to follow navigational instructions and embodied question answering. In this paper, we aim to learn a multitask model capable of jointly learning both tasks, and transferring knowledge of words and their grounding in visual objects across tasks. The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual objects in the visual representations, and align them with each other. This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks. We show that the proposed model outperforms a range of baselines on both tasks in simulated 3D environments. We also show that this disentanglement of representations makes our model modular, interpretable, and allows for zero-shot transfer to instructions containing new words by leveraging object detectors.
Diverse Machine Translation with a Single Multinomial Latent Variable
Authors: Tianxiao Shen, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diversity, Machine Translation, Mixture-of-Experts, Sentence, Translation

There are many ways to translate a sentence into another language. Explicit modeling of such uncertainty may enable better model fitting to the data and it may enable users to express a preference for how to translate a piece of content. Latent variable models are a natural way to represent uncertainty. Prior work investigated the use of multivariate continuous and discrete latent variables, but their interpretation and use for generating a diverse set of hypotheses have been elusive. In this work, we drastically simplify the model, using just a single multinomial latent variable. The resulting mixture of experts model can be trained efficiently via hard-EM and can generate a diverse set of hypothesis by parallel greedy decoding. We perform extensive experiments on three WMT benchmark datasets that have multiple human references, and we show that our model provides a better trade-off between quality and diversity of generations compared to all baseline methods.\footnote{Code to reproduce this work is available at: anonymized URL.}
Visual Imitation with a Minimal Adversary
Authors: Scott Reed, Yusuf Aytar, Ziyu Wang, Tom Paine, Aäron van den Oord, Tobias Pfaff, Sergio Gomez, Alexander Novikov, David Budden, Oriol Vinyals
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Reinforcement Learning, Robot Manipulation

High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.  In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal? We show that adversarial imitation can work well even in this high dimensional observation space. Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation. Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo. The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.  Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines. Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.
Language Modeling with Graph Temporal Convolutional Networks
Authors: Hongyin Luo, Yichen Li, Jie Fu, James Glass
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Graph Neural Network, Language Modeling, Language Modelling

Recently, there have been some attempts to use non-recurrent neural models for language modeling. 
However, a noticeable performance gap still remains. 
We propose a non-recurrent neural language model, dubbed graph temporal convolutional network (GTCN), that relies on graph neural network blocks and convolution operations. While the standard recurrent neural network language models encode sentences sequentially without modeling higher-level structural information, our model regards sentences as graphs and processes input words within a message propagation framework, aiming to learn better syntactic information by inferring skip-word connections. Specifically, the graph network blocks operate in parallel and learn the underlying graph structures in sentences without any additional annotation pertaining to structure knowledge. Experiments demonstrate that the model without recurrence can achieve comparable perplexity results in language modeling tasks and successfully learn syntactic information.
Lipschitz regularized Deep Neural Networks generalize
Authors: Adam M. Oberman, Jeff Calder
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We show that if the usual training loss is augmented by a Lipschitz regularization term, then the networks generalize.  We prove generalization by first establishing a stronger convergence result, along with a rate of convergence.   A second result resolves a question posed in Zhang et al. (2016): how can a model distinguish between the case of clean labels, and randomized labels?  Our answer is that Lipschitz regularization using the Lipschitz constant of the clean data makes this distinction.  In this case, the model learns a different function which we hypothesize correctly fails to learn the dirty labels.
Cohen Welling bases & SO(2)-Equivariant classifiers using Tensor nonlinearity.
Authors: Muthuvel Murugan, K Venkata Subrahmanyam
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

In this paper we propose autoencoder architectures for learning a Cohen-Welling
(CW)-basis for images and their rotations. We use the learned CW-basis to build
a rotation equivariant classifier to classify images. The autoencoder and classi-
fier architectures use only tensor product nonlinearity. The model proposed by
Cohen & Welling (2014) uses ideas from group representation theory, and extracts
a basis exposing irreducible representations for images and their rotations. We
give several architectures to learn CW-bases including a novel coupling AE archi-
tecture to learn a coupled CW-bases for images in different scales simultaneously.
Our use of tensor product nonlinearity is inspired from recent work of Kondor
(2018a). Our classifier has very good accuracy and we use fewer parameters.
Even when the sample complexity to learn a good CW-basis is low we learn clas-
sifiers which perform impressively. We show that a coupled CW-bases in one scale
can be deployed to classify images in a classifier trained and tested on images in
a different scale with only a marginal dip in performance.
Knows When it Doesn’t Know: Deep Abstaining Classifiers
Authors: Sunil Thulasidasan, Tanmoy Bhattacharya, Jeffrey Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training. This allows the  DNN to abstain on confusing or difficult-to-learn examples while improving performance on the non-abstained samples. We show that such deep abstaining classifiers can: (i) learn representations for structured noise -- where noisy training labels or confusing examples are correlated with underlying features -- and then learn to abstain based on such features; (ii) enable robust learning in the presence of arbitrary or unstructured noise by identifying noisy samples; and (iii) be used as an effective out-of-category detector that learns to reliably abstain when presented with samples from  unknown classes. We provide analytical results on loss function behavior that enable automatic tuning of accuracy and coverage, and demonstrate the utility of the deep abstaining classifier using multiple image benchmarks, Results indicate significant improvement in learning in the presence of label noise.
Predicting the Present and Future States of Multi-agent Systems from Partially-observed Visual Data
Authors: Chen Sun, Per Karlsson, Jiajun Wu, Joshua B. Tenenbaum, Kevin Murphy
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.
Modulating transfer between tasks in gradient-based meta-learning
Authors: Erin Grant, Ghassen Jerfel, Katherine Heller, Thomas L. Griffiths
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diversity, Inductive Bias, Meta-Learning

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not mutually beneficial, for instance, when tasks are sufficiently dissimilar or change over time. Here, we use the connection between gradient-based meta-learning and hierarchical Bayes to propose a mixture of hierarchical Bayesian models over the parameters of an arbitrary function approximator such as a neural network. Generalizing the model-agnostic meta-learning (MAML) algorithm, we present a stochastic expectation maximization procedure to jointly estimate parameter initializations for gradient descent as well as a latent assignment of tasks to initializations. This approach better captures the diversity of training tasks as opposed to consolidating inductive biases into a single set of hyperparameters. Our experiments demonstrate better generalization on the standard miniImageNet benchmark for 1-shot classification. We further derive a novel and scalable non-parametric variant of our method that captures the evolution of a task distribution over time as demonstrated on a set of few-shot regression tasks.
Approximation capability of neural networks on sets of probability measures and tree-structured data
Authors: Tomáš Pevný, Vojtěch Kovařík
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: AutoML

This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing kernel Hilbert spaces.  
The work has wide practical consequences for multi-instance learning, where it theoretically justifies some recently proposed constructions.
The result is then extended to Cartesian products, yielding universal approximation theorem for tree-structured domains, which naturally occur in data-exchange formats like JSON, XML, YAML, AVRO, and ProtoBuffer. This has important practical implications, as it enables to automatically create an architecture of neural networks for processing structured data (AutoML paradigms), as demonstrated by an accompanied library for JSON format.