pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Feature Learning under Accuracy Constraint for Domain Generalization
Authors: Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Date: 2019-03-14 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Domain Generalization

Learning domain-invariant representation is a dominant approach for domain generalization. However, previous methods based on domain invariance overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and the invariance. This study proposes a novel method {\em adversarial feature learning under accuracy constraint (AFLAC)}, which maximizes domain invariance within a range that does not interfere with accuracy. Empirical validations show that the performance of AFLAC is superior to that of baseline methods, supporting the importance of considering the dependency and the efficacy of the proposed method to overcome the problem.
Improved Self-Supervised Deep Image Denoising
Authors: Samuli Laine, Jaakko Lehtinen, Timo Aila
Date: 2019-03-14 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Denoising, Image Denoising

We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a "blind spot" in the receptive field, we address two of its  shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise.
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only
Authors: Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Date: 2019-03-14 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Aspect Extraction, Descriptive, Opinion Mining, Sentiment Analysis, Topic Models

Aspect extraction in online product reviews is a key task in sentiment analysis and opinion mining. Training supervised neural networks for aspect extraction is not possible when ground truth aspect labels are not available, while the unsupervised neural topic models fail to capture the particular aspects of interest. In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i.e., keywords that describe an aspect, are available. Our main contributions are as follows. First, we show that current weakly supervised networks fail to leverage the predictive power of the available seed words by comparing them to a simple bag-of-words classifier.  Second, we propose a distillation approach for aspect extraction where the seed words are considered by the bag-of-words classifier (teacher) and distilled to the parameters of a neural network (student). Third, we show that regularization encourages the student to consider non-seed words for classification and, as a result, the student outperforms the teacher, which only considers the seed words. Finally, we empirically show that our proposed distillation approach outperforms (by up to 34.4% in F1 score) previous weakly supervised approaches for aspect extraction in six domains of Amazon product reviews.
De-biasing Weakly Supervised Learning by Regularizing Prediction Entropy
Authors: Dean Wyatte
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Weakly-supervised Learning

We explore the effect of regularizing prediction entropy in a weakly supervised setting with inexact class labels. When underlying data distributions are biased toward a specific subclass, we hypothesize that entropy regularization can be used to bootstrap a training set that mitigates this bias. We conduct experiments over multiple datasets under supervision of an oracle and in a semi-supervised setting finding substantial reductions in training set bias capable of decreasing test error rate. These findings suggest entropy regularization as a promising approach to de-biasing weakly supervised learning systems.
Skip-connection and batch-normalization improve data separation ability
Authors: Yasutaka Furusho, Kazushi Ikeda
Date: 2019-03-20 00:00:00

The ResNet and the batch-normalization (BN) achieved high performance even when only a few labeled data are available. However, the reasons for its high performance are unclear. To clear the reasons, we analyzed the effect of the skip-connection in ResNet and the BN on the data separation ability, which is an important ability for the classification problem. Our results show that, in the multilayer perceptron with randomly initialized weights, the angle between two input vectors converges to zero in an exponential order of its depth, that the skip-connection makes this exponential decrease into a sub-exponential decrease, and that the BN relaxes this sub-exponential decrease into a reciprocal decrease. Moreover, our analysis shows that the preservation of the angle at initialization encourages trained neural networks to separate points from different classes. These imply that the skip-connection and the BN improve the data separation ability and achieve high performance even when only a few labeled data are available.
Invariant Feature Learning by Attribute Perception Matching
Authors: Yusuke Iwasawa, Kei Akuzawa, Yutaka Matsuo
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Attribute

An adversarial feature learning (AFL) is a powerful framework to learn representations invariant to a nuisance attribute, which uses an adversarial game between a feature extractor and a categorical attribute classifier. It theoretically sounds in term of it maximize conditional entropy between attribute and representation. However, as shown in this paper, the AFL often causes unstable behavior that slows down the convergence. We propose an {\em attribute perception matching} as an alternative approach, based on the reformulation of conditional entropy maximization as {\em pair-wise distribution matching}. Although the naive approach for realizing the pair-wise distribution matching requires the significantly large number of parameters, the proposed method requires the same number of parameters with AFL but has a better convergence property. Experiments on both toy and real-world dataset prove that our proposed method converges to better invariant representation significantly faster than AFL.
Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation
Authors: Suman Ravuri, Oriol Vinyals
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Data Augmentation

Recent advances in Generative Adversarial Networks (GANs) – in architectural design,  training strategies,  and empirical tricks – have led nearly photorealistic samples on large-scale datasets such as ImageNet.  In fact, for one model in particular, BigGAN, metrics such as Inception Score or Frechet Inception Distance nearly match those of the dataset, suggesting that these models are close to match-ing the distribution of the training set.   Given the quality of these models,  it is worth understanding to what extent these samples can be used for data augmentation, a task expressed as a long-term goal of the GAN research project.  To that end, we train ResNet-50 classifiers using either purely BigGAN images or mixtures of ImageNet and BigGAN images, and test on the ImageNet validation set.Our  preliminary  results  suggest  both a measured view of  state-of-the-art  GAN quality and highlight limitations of current metrics. Using only BigGAN images, we find that Top-1 and Top-5 error increased by 120% and 384%, respectively, and furthermore, adding more BigGAN data to the ImageNet training set at best only marginally improves classifier performance. Finally, we find that neither Inception Score, nor FID, nor combinations thereof are predictive of classification accuracy.   These results suggest that as GANs are beginning to be deployed in downstream tasks, we should create metrics that better measure downstream task performance.  We propose classification performance as one such metric that, in addition to assessing per-class sample quality, is more suited to such downstream tasks.
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories
Authors: Jeffrey Ling, Nicholas FitzGerald, Livio Baldini Soares, David Weiss, Tom Kwiatkowski
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Entity Typing, Language Modeling, Language Modelling, Learning Word Embeddings, Word Embeddings, World Knowledge

Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code
world knowledge into machine readable knowledge bases tend to be entity-centric,
we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the contexts in which those entities were mentioned.
We show that large scale training of neural models allows us to learn extremely high fidelity entity typing information, which we demonstrate with few-shot reconstruction of Wikipedia categories. Our learning approach is powerful enough
to encode specialized topics such as Giro d’Italia cyclists.
Explanation-Based Attention for Semi-Supervised Deep Active Learning
Authors: Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa
Date: 2019-03-20 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Active Learning

We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting. The proposed attention mechanism is based on recent methods to visually explain predictions made by DNNs. We apply the proposed explanation-based attention to MNIST and SVHN classification. The conducted experiments show accuracy improvements for the original and class-imbalanced datasets with the same number of training examples and faster long-tail convergence compared to uncertainty-based methods.
Reproducing Meta-learning with differentiable closed-form solvers
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: Form, Meta-Learning

In this paper, we present a reproduction of the paper of Bertinetto et al. [2019] "Meta-learning with differentiable closed-form solvers" as part of the ICLR 2019 Reproducibility Challenge. In successfully reproducing the most crucial part of the paper, we reach a performance that is comparable with or superior to the original paper on two benchmarks for several settings. We evaluate new baseline results, using a new dataset presented in the paper. Yet, we also provide multiple remarks and recommendations about reproducibility and comparability.  After we brought our reproducibility work to the authors’ attention, they have updated the original paper on which this work is based and released code as well. Our contributions mainly consist in reproducing the most important results of their original paper, in giving insight in the reproducibility and in providing a first open-source implementation.
Reproducibility in Machine Learning for Health
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: BIG-bench Machine Learning

Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning. In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.  Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.
Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: Few-Shot Learning

We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.  We propose a methodology for testing for statistical differences in model performances under several replications. To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM. We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time. We find that the selected implementations exhibit stability across random seed, and repeats.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: Disentanglement

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms.
In this paper, we provide a sober look on recent progress in the field and challenge some common assumptions.
We train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets.
We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision.
Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. 
Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets.
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: Link Prediction, Network Embedding, Prediction

Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space. These representations are then used for a variety of downstream prediction tasks. Link prediction is one of the most popular choices for assessing the performance of NE methods. However, the complexity of link prediction requires a carefully designed evaluation pipeline to provide consistent, reproducible and comparable results. We argue this has not been considered sufficiently in recent works. The main goal of this paper is to overcome difficulties associated with evaluation pipelines and reproducibility of results. We introduce EvalNE, an evaluation framework to transparently assess and compare the performance of NE methods on link prediction. EvalNE provides automation and abstraction for tasks such as hyper-parameter tuning, model validation, edge sampling, computation of edge embeddings and model validation. The framework integrates efficient procedures for edge and non-edge sampling and can be used to easily evaluate any off-the-shelf embedding method. The framework is freely available as a Python toolbox. Finally, demonstrating the usefulness of EvalNE in practice, we conduct an empirical study in which we try to replicate and analyse experimental sections of several influential papers.
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consistently checking the statistical significance of experimental results is the first mandatory step towards reproducible science. This paper presents a hitchhiker's guide to rigorous comparisons of reinforcement learning algorithms. After introducing the concepts of statistical testing, we review the relevant statistical tests and compare them empirically in terms of false positive rate and statistical power as a function of the sample size (number of seeds) and effect size. We further investigate the robustness of these tests to violations of the most common hypotheses (normal distributions, same distributions, equal variances). Beside simulations, we compare empirical distributions obtained by running Soft-Actor Critic and Twin-Delayed Deep Deterministic Policy Gradient on Half-Cheetah. We conclude by providing guidelines and code to perform rigorous comparisons of RL algorithm performances.
simple_rl: Reproducible Reinforcement Learning in Python
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: Philosophy, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conducting reinforcement-learning experiments can be a complex and timely process. A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof. In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity. The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments. This paper gives an overview  of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.
Minigo: A Case Study in Reproducing Reinforcement Learning Research
Authors: Anonymous
Date: 2019-03-06 00:00:00
Proceeding: ICLR Workshop RML 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field. In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game. We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs. The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure. With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs. We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.
Multi-agent query reformulation: Challenges and the role of diversity
Authors: Rodrigo Nogueira, Jannis Bulian, Massimiliano Ciaramita
Date: 2019-03-16 00:00:00
Proceeding: ICLR Workshop drlStructPred 2019 5
Tasks: Diversity, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Structured Prediction

We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer. Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set. Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as
an ensemble of agents trained on the full data. We evaluate on the tasks of document retrieval and question answering. The
improved performance seems due to the increased diversity of reformulation strategies. This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind. However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results. Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes. At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.
Buy 4 REINFORCE Samples, Get a Baseline for Free!
Authors: Wouter Kool, Herke van Hoof, Max Welling
Date: 2019-03-16 00:00:00
Proceeding: ICLR Workshop drlStructPred 2019 5
Tasks: Prediction, Structured Prediction

REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective. However, the common case of sampling one prediction per datapoint (input) is data-inefficient. We show that by drawing multiple samples (predictions) per datapoint, we can learn with significantly less data, as we freely obtain a REINFORCE baseline to reduce variance. Additionally we derive a REINFORCE estimator with baseline, based on sampling without replacement. Combined with a recent technique to sample sequences without replacement using Stochastic Beam Search, this improves the training procedure for a sequence model that predicts the solution to the Travelling Salesman Problem.
Learning proposals for sequential importance samplers using reinforced variational inference
Authors: Zafarali Ahmed, Arjun Karuvally, Doina Precup, Simon Gravel
Date: 2019-03-16 00:00:00
Proceeding: ICLR Workshop drlStructPred 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Structured Prediction, Variational Inference

The problem of inferring unobserved values in a partially observed trajectory from a stochastic process can be considered as a structured prediction problem. Traditionally inference is conducted using heuristic-based Monte Carlo methods. This work considers learning heuristics by leveraging a connection between policy optimization reinforcement learning and approximate inference. In particular, we learn proposal distributions used in importance samplers by casting it as a variational inference problem. We then rewrite the variational lower bound as a policy optimization problem similar to Weber et al. (2015) allowing us to transfer techniques from reinforcement learning. We apply this technique to a simple stochastic process as a proof-of-concept and show that while it is viable, it will require more engineering effort to scale inference for rare observations