Publications

Emergent Communication under Competition
Michael Noukhovitch
Travis LaCroix
Angeliki Lazaridou
Aaron Courville
2020-12-31
AAMAS (published)
doi.org
arxiv.org
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu
Wujie Wang
Shitong Luo
Chence Shi
Yoshua Bengio
Rafael Gomez-Bombarelli
Jian Tang
Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing app… (see more)roaches are usually divided into two steps by first predicting the distances between atoms and then generating a 3D structure through optimizing a distance geometry problem. However, the distances predicted with such two-stage approaches may not be able to consistently preserve the geometry of local atomic neighborhoods, making the generated structures unsatisfying. In this paper, we propose an end-to-end solution for molecular conformation prediction called ConfVAE based on the conditional variational autoencoder framework. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program. Extensive experiments on several benchmark data sets prove the effectiveness of our proposed approach over existing state-of-the-art approaches. Code is available at https://github.com/MinkaiXu/ConfVAE-ICML21
2020-12-31
ICML (published)
doi.org
proceedings.mlr.press
Enjeux juridiques propres au modèle émergent des patients accompagnateurs dans les milieux de soins au Québec (Legal Issues Arising from the Emerging Model of Accompanying Patients in the Quebec Healthcare System)
Léa Boutrouille
Catherine Régis
Marie-Pascale Pomey
2020-12-31
SSRN Electronic Journal (published)
doi.org
Episodes Meta Sequence S 2 Fast Update Slow Update Fast Update Slow Update
Kanika Madan
Nan Rosemary Ke
Anirudh Goyal
Bernhard Schölkopf
Yoshua Bengio
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (see more)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
(published)
www.semanticscholar.org
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 … (see 37 more)
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
Yoshua Bengio
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… (see more)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
(published)
www.semanticscholar.org
Exploring the Wasserstein metric for survival analysis
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Heloise Cardinal
Andrea Lodi
Yoshua Bengio
2020-12-31
SPACA (published)
proceedings.mlr.press
proceedings.mlr.press
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
Anirudh Goyal
Alex Lamb
Phanideep Gampa
Philippe Beaudoin
Charles Blundell
Sergey Levine
Yoshua Bengio
Michael Mozer
2020-12-31
ICLR (published)
openreview.net
openreview.net
Fast and Slow Learning of Recurrent Independent Mechanisms
Kanika Madan
Rosemary Nan Ke
Anirudh Goyal
Bernhard Schölkopf
Yoshua Bengio
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (see more)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, meta-parameters. 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
ICLR (published)
doi.org
openreview.net
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… (see more)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
(published)
www.semanticscholar.org
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Emmanuel Bengio
Moksh Jain
Maksym Korablyov
Doina Precup
Yoshua Bengio
This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions… (see more), such that the probability of generating an object is proportional to a given positive reward for that object. Whereas standard return maximization tends to converge to a single return-maximizing sequence, there are cases where we would like to sample a diverse set of high-return solutions. These arise, for example, in black-box function optimization when few rounds are possible, each with large batches of queries, where the batches should be diverse, e.g., in the design of new molecules. One can also see this as a problem of approximately converting an energy function to a generative distribution. While MCMC methods can achieve that, they are expensive and generally only perform local exploration. Instead, training a generative policy amortizes the cost of search during training and yields to fast generation. Using insights from Temporal Difference learning, we propose GFlowNet, based on a view of the generative process as a flow network, making it possible to handle the tricky case where different trajectories can yield the same final state, e.g., there are many ways to sequentially add atoms to generate some molecular graph. We cast the set of trajectories as a flow and convert the flow consistency equations into a learning objective, akin to the casting of the Bellman equations into Temporal Difference methods. We prove that any global minimum of the proposed objectives yields a policy which samples from the desired distribution, and demonstrate the improved performance and diversity of GFlowNet on a simple domain where there are many modes to the reward function, and on a molecule synthesis task.
2020-12-31
NeurIPS (published)
doi.org
openreview.net
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
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… (see more)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
arXiv (preprint)
doi.org
arxiv.org
Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare
Arash Shaban-Nejad
Martin Michalowski
John S. Brownstein
David L Buckeridge
2020-12-31
IEEE journal of biomedical and health informatics (published)
doi.org