Publications

R´ejean Ducharme

P Vincent

Kevin Clark

Quoc Minh-Thang Luong

V. Le

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Adam Fisch

Alon Talmor

Robin Jia

Minjoon Seo

Michael R. Glass

A. Gliozzo

Rishav Chakravarti

Ian J Goodfellow

Jean Pouget-Abadie … (voir 39 de plus)

Mehdi Mirza

Serhii Havrylov

Ivan Titov. 2017

Emergence

Jun-Tao He

Jiatao Gu

Jiajun Shen

Marc’Aurelio

Matthew Henderson

I. Casanueva

Nikola Mrkˇsi´c

Pei-hao Su

Tsung-Hsien Wen

Ivan Vuli´c

Yikang Shen

Yi Tay

Che Zheng

Dara Bahri

Donald

Metzler Aaron

Courville

Structformer

Ashish Vaswani

Noam M. Shazeer

Niki Parmar

Thomas Wolf

Lysandre Debut

Julien Victor Sanh

Clement Chaumond

Anthony Delangue

Pier-339 Moi

Tim ric Cistac

R´emi Rault

Morgan Louf

Qizhe Xie

Eduard H. Hovy

Silei Xu

Sina Jandaghi Semnani

Giovanni Campagna

Pretrained language models have signiﬁcantly 001 improved the performance of down-stream 002 language understanding tasks, including ex-00… (voir plus)3 tractive question answering, by providing 004 high-quality contextualized word embeddings. 005 However, training question answering models 006 still requires large amounts of annotated data 007 for speciﬁc domains. In this work, we pro-008 pose a cooperative, self-play learning frame-009 work, REGEX, for automatically generating 010 more non-trivial question-answer pairs to im-011 prove model performance. REGEX is built 012 upon a masked answer extraction task with an 013 interactive learning environment containing an 014 answer entity REcognizer, a question Gener-015 ator, and an answer EXtractor. Given a pas-016 sage with a masked entity, the generator gen-017 erates a question around the entity, and the 018 extractor is trained to extract the masked en-019 tity with the generated question and raw texts. 020 The framework allows the training of question 021 generation and answering models on any text 022 corpora without annotation. We further lever-023 age a reinforcement learning technique to re-024 ward generating high-quality questions and to 025 improve the answer extraction model’s perfor-026 mance. Experiment results show that REGEX 027 outperforms the state-of-the-art (SOTA) pre-028 trained language models and transfer learning 029 approaches on standard question-answering 030 benchmarks, and yields the new SOTA per-031 formance under given model size and transfer 032 learning settings. 033

2020-12-31

(publié)

Correcting Momentum in Temporal Difference Learning

Emmanuel Bengio

Joelle Pineau

Doina Precup

A common optimization tool used in deep reinforcement learning is momentum, which consists in accumulating and discounting past gradients, r… (voir plus)eapplying them at each iteration. We argue that, unlike in supervised learning, momentum in Temporal Difference (TD) learning accumulates gradients that become doubly stale: not only does the gradient of the loss change due to parameter updates, the loss itself changes due to bootstrapping. We first show that this phenomenon exists, and then propose a first-order correction term to momentum. We show that this correction term improves sample efficiency in policy evaluation by correcting target value drift. An important insight of this work is that deep RL methods are not always best served by directly importing techniques from the supervised setting.

2020-12-31

arXiv (prépublication)

openreview.net

DATA-EFFICIENT REINFORCEMENT LEARNING

R Devon Hjelm

Philip Bachman

Aaron Courville

Data efficiency poses a major challenge for deep reinforcement learning. We approach this issue from the perspective of self-supervised repr… (voir plus)esentation learning, leveraging reward-free exploratory data to pretrain encoder networks. We employ a novel combination of latent dynamics modelling and goal-reaching objectives, which exploit the inherent structure of data in reinforcement learning. We demonstrate that our method scales well with network capacity and pretraining data. When evaluated on the Atari 100k data-efficiency benchmark, our approach significantly outperforms previous methods combining unsupervised pretraining with task-specific finetuning, and approaches human-level performance.

2020-12-31

(publié)

Deep LDA-Pruned Nets for Efﬁcient Facial Gender Classiﬁcation

Qing Tian

Tal Arbel

James J. Clark

Many real-time tasks, such as human-computer interac-tion, require fast and efﬁcient facial gender classiﬁcation. Although deep CNN nets… (voir plus) have been very effective for a mul-titude of classiﬁcation tasks, their high space and time de-mands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efﬁciency while maintaining high accuracy. Our net is pruned from the VGG-16 model [35] starting from the last convolutional (conv) layer where we ﬁnd neuron activations are highly uncorrelated given the gender. Through Fisher’s Linear Discriminant Analysis (LDA) [8], we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classiﬁcation, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5 3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method [12] (as well as two smaller nets [20, 24]) in terms of accuracy loss and convolutional layers pruning rate are also provided.

2020-12-31

(publié)

Emergent Communication under Competition

Michael Noukhovitch

Travis LaCroix

Angeliki Lazaridou

Aaron Courville

2020-12-31

AAMAS (publié)

Episodes Meta Sequence S 2 Fast Update Slow Update Fast Update Slow Update

Kanika Madan

Nan Rosemary Ke

Anirudh Goyal

Bernhard Schölkopf

Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (voir plus)nt interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, metaparameters.We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.

2020-12-31

(publié)

Explaining by Analogy: Case-based Abductive Natural Language Inference

Ruben Cartuyvels

Graham Spinks

Marie Francine

Peter Clark

Isaac Cowhey

Oren Etzioni

Tushar Khot

Rajarshi Das

Ameya Godbole

Shehzaad Dhuliawala

Manzil Zaheer

Andrew McCallum

Dung Ngoc Thai

Ameya

Ethan Godbole

Jay-Yoon Perez

Lee

Lizhen

Ramón López De Mántaras

David Mcsherry … (voir 37 de plus)

David Bridge

Barry Leake

Susan Smyth

Craw.

Boi

Maryalice Faltings

Michael T Maher

Ken-552 Cox

Dorottya Demszky

Kelvin Guu

Percy Liang

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Daniel Fried

Peter Jansen

Gus Hahn-Powell

Higher-575

Rebecca Emilie Sharp

M. Surdeanu

Zhengnan Xie

Sebastian Thiem

Jaycie Ryrholm Martin

Eliz-721 abeth Wainwright

Steven Marmorstein

Wenhan Xiong

Xiang Lorraine Li

Srini Iyer

Jingfei Du

Vikas Yadav

Steven Bethard

Zhilin Yang

Peng Qi

Saizheng Zhang

William W Cohen

Russ Salakhutdinov

Existing accounts of explanation emphasise 001 the role of prior experience and analogy in 002 the solution of new problems. However, most 0… (voir plus)03 of the contemporary models for multi-hop tex-004 tual inference construct explanations consider-005 ing each test case in isolation. This paradigm 006 is known to suffer from semantic drift, which 007 causes the construction of spurious explana-008 tions leading to wrong predictions. In con-009 trast, we propose an abductive framework for 010 multi-hop inference that adopts the retrieve - 011 reuse - revise paradigm largely studied in case-012 based reasoning . Speciﬁcally, we present 013 ETNA ( E xplana t io n by A nalogy), a novel 014 model that addresses unseen inference prob-015 lems by retrieving and adapting prior expla-016 nations from similar training examples. We 017 empirically evaluate the case-based abductive 018 framework on downstream commonsense and 019 scientiﬁc reasoning tasks. Our experiments 020 demonstrate that ETNA can be effectively in-021 tegrated with sparse and dense encoding mech-022 anisms or downstream transformers, achiev-023 ing strong performance when compared to ex-024 isting explainable approaches. Moreover, we 025 study the impact of the retrieve - reuse - revise 026 paradigm on explainability and semantic drift, 027 showing that it boosts the quality of the con-028 structed explanations, resulting in improved 029 downstream inference performance. 030

2020-12-31

(publié)

Exploring the Wasserstein metric for survival analysis

Tristan Sylvain

Margaux Luck

Joseph Paul Cohen

Heloise Cardinal

Andrea Lodi

2020-12-31

Signal Processing and Communications Applications Conference (publié)

proceedings.mlr.press

Extendable and invertible manifold learning with geometry regularized autoencoders

Andrés F. Duque

Sacha Morin

Guy Wolf

Kevin Moon

A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, esp… (voir plus)ecially for faithfully visualizing data in two or three dimensions. Common approaches to this task use kernel methods for manifold learning. However, these methods typically only provide an embedding of fixed input data and cannot extend to new data points. Autoencoders have also recently become popular for representation learning. But while they naturally compute feature extractors that are both extendable to new data and invertible (i.e., reconstructing original features from latent representation), they have limited capabilities to follow global intrinsic geometry compared to kernel-based manifold learning. We present a new method for integrating both approaches by incorporating a geometric regularization term in the bottleneck of the autoencoder. Our regularization, based on the diffusion potential distances from the recently-proposed PHATE visualization method, encourages the learned latent representation to follow intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and reconstruction of data in the original feature space from latent coordinates. We compare our approach with leading kernel methods and autoencoder models for manifold learning to provide qualitative and quantitative evidence of our advantages in preserving intrinsic structure, out of sample extension, and reconstruction. Our method is easily implemented for big-data applications, whereas other methods are limited in this regard.

2020-12-31

IEEE International Conference on Big Data (inconnu)

Finding Archetypal Spaces Using Neural Networks

David van Dijk

Daniel B. Burkhardt

Matthew Amodio

Alexander Tong

Guy Wolf

Smita Krishnaswamy

Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of "pure types" or a… (voir plus)rchetypes. These archetypes represent extrema of a data space in which there is a trade-off between features, such as in biology where different combinations of traits provide optimal fitness for different environments. Existing methods for archetypal analysis work well when a linear relationship exists between the feature space and the archetypal space. However, such methods are not applicable to systems where the feature space is generated non-linearly from the combination of archetypes, such as in biological systems or image transformations. Here, we propose a reformulation of the problem such that the goal is to learn a non-linear transformation of the data into a latent archetypal space. To solve this problem, we introduce Archetypal Analysis network (AAnet), which is a deep neural network framework for learning and generating from a latent archetypal representation of data. We demonstrate state-of-the-art recovery of ground-truth archetypes in non-linear data domains, show AAnet can generate from data geometry rather than from data density, and use AAnet to identify biologically meaningful archetypes in single-cell gene expression data.

2020-12-31

IEEE International Conference on Big Data (inconnu)

Finite time analysis of temporal difference learning with linear function approximation: the tail averaged case

Gandharv Patil

Prashanth L.A.

Doina Precup

In this paper, we study the ﬁnite-time behaviour of temporal difference (TD) learning algorithms when combined with tail-averaging, and pr… (voir plus)esent instance dependent bounds on the parameter error of the tail-averaged TD iterate. Our error bounds hold in expectation as well as with high probability, exhibit a sharper rate of decay for the initial error (bias), and are comparable with existing bounds in the literature.

2020-12-31

(publié)

Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification

Charles A. Kantor

Marta Skreta

Brice Rauby

Léonard Boussioux

Emmanuel Jehanno

Alexandra Luccioni

David Rolnick

Hugues Talbot

Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute deta… (voir plus)ils. Our primary challenges come from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of wildlife, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.

2020-12-31

IJCAI Workshop on AI for Social Good (inconnu)