pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting the Generalization of Adaptive Gradient Methods
Authors: Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: BIG-bench Machine Learning

A commonplace belief in the machine learning community is that using adaptive gradient methods hurts generalization. We re-examine this belief both theoretically and experimentally, in light of insights and trends from recent years.
We revisit some previous oft-cited experiments and theoretical accounts in more depth, and provide a new set of experiments in larger-scale, state-of-the-art settings. We conclude that with proper tuning, the improved training performance of adaptive optimizers does not in general carry an overfitting penalty, especially in contemporary deep learning. Finally, we synthesize a ``user's guide'' to adaptive optimizers, including some proposed modifications to AdaGrad to mitigate some of its empirical shortcomings.
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
Authors: Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov
Date: 2020-01-01 00:00:00
Proceeding: ICML 2020 1
Tasks: Discrete Choice Models, Econometrics

Discrete choice models with unobserved heterogeneity are commonly used Econometric models for dynamic Economic behavior which have been adopted in practice to predict behavior of individuals and firms from schooling and job choices to strategic decisions in market competition. These models  feature optimizing agents who choose among a finite set of options in a sequence of periods and receive choice-specific payoffs that depend on both variables that are observed by the agent and recorded in the data and variables that are only observed by the agent but not recorded in the data. Existing work in Econometrics assumes that optimizing agents are fully rational and requires finding a functional fixed point to find the optimal policy. We show that in an important class of discrete choice models the value function is globally concave in the policy. That means that simple algorithms that do not require fixed point computation, such as the policy gradient algorithm, globally converge to the optimal policy. This finding can both be used to relax behavioral assumption regarding the optimizing agents and to facilitate Econometric analysis of dynamic behavior. In particular, we demonstrate significant computational advantages in using a simple implementation policy gradient algorithm over existing "nested fixed point" algorithms used in Econometrics.
Unifying Graph Convolutional Networks as Matrix Factorization
Authors: Zhaocheng Liu, Qiang Liu, Haoli Zhang, Jun Zhu
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Distributed Computing, Node Classification

In recent years, substantial progress has been made on graph convolutional networks (GCN). In this paper, for the first time, we theoretically analyze the connections between GCN and matrix factorization (MF), and unify GCN as matrix factorization with co-training and unitization. Moreover, under the guidance of this theoretical analysis, we propose an alternative model to GCN named Co-training and Unitized Matrix Factorization (CUMF). The correctness of our analysis is verified by thorough experiments. The experimental results show that CUMF achieves similar or superior performances compared to GCN. In addition, CUMF inherits the benefits of MF-based methods to naturally support constructing mini-batches, and is more friendly to distributed computing comparing with GCN. The distributed CUMF on semi-supervised node classification significantly outperforms distributed GCN methods. Thus, CUMF greatly benefits large scale and complex real-world applications.
Targeted sampling of enlarged neighborhood via Monte Carlo tree search for TSP
Authors: Zhang-Hua Fu, Kai-Bin Qiu, Meng Qiu, Hongyuan Zha
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: BIG-bench Machine Learning, Combinatorial Optimization

The travelling salesman problem (TSP) is a well-known combinatorial optimization problem with a variety of real-life applications. We tackle TSP by incorporating machine learning methodology and leveraging the variable neighborhood search strategy. More precisely, the search process is considered as a Markov decision process (MDP), where a 2-opt local search is used to search within a small neighborhood, while a Monte Carlo tree search (MCTS) method (which iterates through simulation, selection and back-propagation steps), is used to sample a number of targeted actions within an enlarged neighborhood. This new paradigm clearly distinguishes itself from the existing machine learning (ML) based paradigms for solving the TSP, which either uses an end-to-end ML model, or simply applies traditional techniques after ML for post optimization. Experiments based on two public data sets show that, our approach clearly dominates all the existing learning based TSP algorithms in terms of performance, demonstrating its high potential on the TSP. More importantly, as a general framework without complicated hand-crafted rules, it can be readily extended to many other combinatorial optimization problems.
Deep Randomized Least Squares Value Iteration
Authors: Guy Adam, Tom Zahavy, Oron Anschel, Nahum Shimkin
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration while learning representations is one of the main challenges Deep
Reinforcement Learning (DRL) faces today. As the learned representation is dependant in the observed data, the exploration strategy has a crucial role. The popular DQN algorithm has improved significantly the capabilities of Reinforcement
Learning (RL) algorithms to learn state representations from raw data, yet, it uses
a naive exploration strategy which is statistically inefficient. The Randomized
Least Squares Value Iteration (RLSVI) algorithm (Osband et al., 2016), on the
other hand, explores and generalizes efficiently via linearly parameterized value
functions. However, it is based on hand-designed state representation that requires
prior engineering work for every environment. In this paper, we propose a Deep
Learning adaptation for RLSVI. Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by a
DQN agent. As the representation is being optimized during the learning process,
a key component for the suggested method is a likelihood matching mechanism,
which adapts to the changing representations. We demonstrate the importance of
the various properties of our algorithm on a toy problem and show that our method
outperforms DQN in five Atari benchmarks, reaching competitive results with the
Rainbow algorithm.
Discriminator Based Corpus Generation for General Code Synthesis
Authors: Alexander Wild, Barry Porter
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

Current work on neural code synthesis consists of increasingly sophisticated architectures being trained on highly simplified domain-specific languages, using uniform sampling across program space of those languages for training. By comparison, program space for a C-like language is vast, and extremely sparsely populated in terms of `useful' functionalities; this requires a far more intelligent approach to corpus generation for effective training. We use a genetic programming approach using an iteratively retrained discriminator to produce a population suitable as labelled training data for a neural code synthesis architecture. We demonstrate that use of a discriminator-based training corpus generator, trained using only unlabelled problem specifications in classic Programming-by-Example format, greatly improves network performance compared to current uniform sampling techniques.
MMD GAN with Random-Forest Kernels
Authors: Tao Huang, Zhen Han, Xu Jia, Hanyuan Hang
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Ensemble Learning

In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN. Different from common forests with deterministic routings, a probabilistic routing variant is used in our innovated random-forest kernel, which is possible to merge with the CNN frameworks. Our proposed random-forest kernel has the following advantages: From the perspective of random forest, the output of GAN discriminator can be viewed as feature inputs to the forest, where each tree gets access to merely a fraction of the features, and thus the entire forest benefits from ensemble learning. In the aspect of kernel method, random-forest kernel is proved to be characteristic, and therefore suitable for the MMD structure. Besides, being an asymmetric kernel, our random-forest kernel is much more flexible, in terms of capturing the differences between distributions. Sharing the advantages of CNN, kernel method, and ensemble learning, our random-forest kernel based MMD GAN obtains desirable empirical performances on CIFAR-10, CelebA and LSUN bedroom data sets. Furthermore, for the sake of completeness, we also put forward comprehensive theoretical analysis to support our experimental results.
Enhancing Language Emergence through Empathy
Authors: Marie Ossenkopf
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Deep Reinforcement Learning, Language Acquisition, Reinforcement Learning

The emergence of language in multi-agent settings is a promising research direction to ground natural language in simulated agents. If AI would be able to understand the meaning of language through its using it, it could also transfer it to other situations flexibly. That is seen as an important step towards achieving general AI. The scope of emergent communication is so far, however, still limited. It is necessary to enhance the learning possibilities for skills associated with communication to increase the emergable complexity. We took an example from human language acquisition and the importance of the empathic connection in this process. We propose an approach to introduce the notion of empathy to multi-agent deep reinforcement learning. We extend existing approaches on referential games with an auxiliary task for the speaker to predict the listener's mind change improving the learning time. Our experiments show the high potential of this architectural element by doubling the learning speed of the test setup.
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach
Authors: Yingjun Pei, Xinwen Hou
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

The information bottleneck principle is an elegant and useful approach to representation learning. In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms.We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound. Then, we maximize this lower bound with the Stein variational (SV) gradient method. 
We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO). Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly. Finally, we study the information-bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE).
We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process. We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.
Metagross: Meta Gated Recursive Controller Units for Sequence Modeling
Authors: Yi Tay, Yikang Shen, Alvin Chan, Yew Soon Ong
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Code Generation, Inductive Bias, Machine Translation, Music Modeling, Semantic Parsing, Translation

This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit. Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion. This can be interpreted as a form of meta-gating and recursively parameterizing a recurrent model. We postulate that our proposed inductive bias provides modeling benefits pertaining to learning with inherently hierarchically-structured sequence data (e.g., language, logical or music tasks). To this end, we conduct extensive experiments on recursive logic tasks (sorting, tree traversal, logical inference), sequential pixel-by-pixel classification, semantic parsing, code generation, machine translation and polyphonic music modeling, demonstrating the widespread utility of the proposed approach, i.e., achieving state-of-the-art (or close) performance on all tasks.
Incorporating Horizontal Connections in Convolution by Spatial Shuffling
Authors: Ikki Kishida, Hideki Nakayama
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

Convolutional Neural Networks (CNNs) are composed of multiple convolution layers and show elegant performance in vision tasks.
The design of the regular convolution is based on the Receptive Field (RF) where the information within a specific region is processed.
In the view of the regular convolution's RF, the outputs of neurons in lower layers with smaller RF are bundled to create neurons in higher layers with larger RF. 
As a result, the neurons in high layers are able to capture the global context even though the neurons in low layers only see the local information.
However, in lower layers of the biological brain, the information outside of the RF changes the properties of neurons.
In this work, we extend the regular convolution and propose spatially shuffled convolution (ss convolution).
In ss convolution, the regular convolution is able to use the information outside of its RF by spatial shuffling which is a simple and lightweight operation.
We perform experiments on CIFAR-10 and ImageNet-1k dataset, and show that ss convolution improves the classification performance across various CNNs.
Generative Adversarial Networks For Data Scarcity Industrial Positron Images With Attention
Authors: Mingwei Zhu, Min Zhao, Min Yao, Ruipeng Guo
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Transfer Learning

In the industrial field, the positron annihilation is not affected by complex environment, and the gamma-ray photon penetration is strong, so the nondestructive detection of industrial parts can be realized. Due to the poor image quality caused by gamma-ray photon scattering, attenuation and short sampling time in positron process, we propose the idea of combining deep learning to generate positron images with good quality and clear details by adversarial nets. The structure of the paper is as follows: firstly, we encode to get the hidden vectors of medical CT images based on transfer Learning, and use PCA to extract positron image features. Secondly, we construct a positron image memory based on attention mechanism as a whole input to the adversarial nets which uses medical hidden variables as a query. Finally, we train the whole model jointly and update the input parameters until convergence. Experiments have proved the possibility of generating rare positron images for industrial non-destructive testing using countermeasure networks, and good imaging results have been achieved.
ISBNet: Instance-aware Selective Branching Networks
Authors: Shaofeng Cai, Yao Shu, Wei Wang, Gang Chen, Beng Chin Ooi
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Neural Architecture Search

Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search (NAS). Although remarkable efficiency and accuracy have been achieved, existing expert designed and NAS models neglect the fact that input instances are of varying complexity and thus different amounts of computation are required. Inference with a fixed model that processes all instances through the same transformations would incur computational resources unnecessarily. Customizing the model capacity in an instance-aware manner is required to alleviate such a problem. In this paper, we propose a novel Instance-aware Selective Branching Network-ISBNet to support efficient instance-level inference by selectively bypassing transformation branches of insignificant importance weight. These weights are dynamically determined by a lightweight hypernetwork SelectionNet and recalibrated by gumbel-softmax for sparse branch selection. Extensive experiments show that ISBNet achieves extremely efficient inference in terms of parameter size and FLOPs comparing to existing networks. For example, ISBNet takes only 8.70% parameters and 31.01% FLOPs of the efficient network MobileNetV2 with comparable accuracy on CIFAR-10.
BERT-AL: BERT for Arbitrarily Long Document Understanding
Authors: Ruixuan Zhang, Zhuoyu Wei, Yu Shi, Yining Chen
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: document understanding, Text Summarization

Pretrained language models attract lots of attentions, and they take advantage of the two-stages training process: pretraining on huge corpus and finetuning on specific tasks. Thereinto, BERT (Devlin et al., 2019) is a Transformer (Vaswani et al., 2017) based model and has been the state-of-the-art for many kinds of Nature Language Processing (NLP) tasks. However, BERT cannot take text longer than the maximum length as input since the maximum length is predefined during pretraining. When we apply BERT to long text tasks, e.g., document-level text summarization: 1) Truncating inputs by the maximum sequence length will decrease performance, since the model cannot capture long dependency and global information ranging the whole document. 2) Extending the maximum length requires re-pretraining which will cost a mass of time and computing resources. What's even worse is that the computational complexity will increase quadratically with the length, which will result in an unacceptable training time. To resolve these problems, we propose to apply Transformer to only model local dependency and recurrently capture long dependency by inserting multi-channel LSTM into each layer of BERT. The proposed model is named as BERT-AL (BERT for Arbitrarily Long Document Understanding) and it can accept arbitrarily long input without re-pretraining from scratch. We demonstrate BERT-AL's effectiveness on text summarization by conducting experiments on the CNN/Daily Mail dataset. Furthermore, our method can be adapted to other Transformer based models, e.g., XLNet (Yang et al., 2019) and RoBERTa (Liu et al., 2019), for various NLP tasks with long text.
NEURAL EXECUTION ENGINES
Authors: Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence. There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set. We study this problem through the lens of fundamental computer science problems: sorting and graph processing. We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization. We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy. Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.
Integrative Tensor-based Anomaly Detection System For Satellites
Authors: Youjin Shin, Sangyup Lee, Shahroz Tariq, Myeong Shin Lee, OkchulJung, Daewon Chung, Simon Woo
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Anomaly Detection

Detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems. Although there have been several studies in detecting anomalies based on rule-based or machine learning-based approaches for satellite systems, a tensor-based decomposition method has not been extensively explored for anomaly detection. In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system. Because of the high risk and cost, detecting anomalies in a satellite system is crucial. We construct 3rd-order tensors with telemetry data collected from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies. Our result shows that our tensor-based approach can be effective in achieving higher accuracy and reducing false positives in detecting anomalies as compared to other existing approaches.
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients
Authors: Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel, Ghassan AlRegib
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Anomaly Detection, Attribute, Benchmarking

Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data. We attribute this vulnerability to the limitations inherent to activation-based representation. To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information. In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information. To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations. We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes. Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets. The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.
Adversarial Paritial Multi-label Learning
Authors: Yan Yan, Yuhong Guo
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Decoder, Generative Adversarial Network, Multi-Label Learning

Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encoder-decoder framework for partial multi-label learning. The PML-GAN model uses a disambiguation network to identify noisy labels and uses a multi-label prediction network to map the training instances to the disambiguated label vectors, while deploying a generative adversarial network as an inverse mapping from label vectors to data samples in the input feature space. The learning of the overall model corresponds to a minimax adversarial game, which enhances the correspondence of input features with the output labels. Extensive experiments are conducted on multiple datasets, while the proposed model demonstrates the state-of-the-art performance for partial multi-label learning.