Publications

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.

Germán Abrevaya

Jean-christophe Gagnon-audet

Aleksandr Y. Aravkin

Peng Zheng

James R. Kozloski

Pablo Polosecki

Guillaume Lajoie

David D. Cox

Silvina Ponce Dawson

Guillermo A. Cecchi

Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (voir plus)linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.

2021-06-06

Neural Computation (inconnu)

CMIM: Cross-Modal Information Maximization For Medical Imaging

Tristan Sylvain

Francis Dutil

Tess Berthier

Lisa Di Jorio

Margaux Luck

R Devon Hjelm

Yoshua Bengio

In hospitals, data are siloed to specific information systems that make the same information available under different modalities such as th… (voir plus)e different medical imaging exams the patient undergoes (CT scans, MRI, PET, Ultrasound, etc.) and their associated radiology reports. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. By maximizing cross-modal information at train time, we are able to outperform several state-of-the-art baselines in two different settings, medical image classification, and segmentation. In particular, our method is shown to have a strong impact on the inference-time performance of weaker modalities.

2021-06-05

IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)

Double-Linear Thompson Sampling for Context-Attentive Bandits

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Yasaman Khazaeni

In this paper, we analyze and extend an online learning frame-work known as Context-Attentive Bandit, motivated by various practical applica… (voir plus)tions, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.

2021-06-05

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Toward Skills Dialog Orchestration with Online Learning

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Mayank Agarwal

Yasaman Khazaeni

Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orc… (voir plus)hestrate multiple independently trained dialog agents, or skills, to create a unified system is of particular significance. In this work, we study the task of online posterior dialog orchestration, where we define posterior orchestration as the task of selecting a subset of skills which most appropriately answer a user input using features extracted from both the user input and the individual skills. To account for the various costs associated with extracting skill features, we consider online posterior orchestration under a skill execution budget. We formalize this setting as Context Attentive Bandit with Observations (CABO), a variant of context attentive bandits, and evaluate it on proprietary conversational datasets.

2021-06-05

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Multimodal dynamics modeling for off-road autonomous vehicles

Jean-François Tremblay

Travis Manderson

Aurélio Noca

Gregory Dudek

David Meger

Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robo… (voir plus)t in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot's environment is thus crucial when building a model to perform predictions about the robot's dynamics with the goal of doing motion planning. We design a model capable of long-horizon motion predictions, leveraging vision, lidar and proprioception, which is robust to arbitrarily missing modalities at test time. We demonstrate in simulation that our model is able to leverage vision to predict traction changes. We then test our model using a real-world challenging dataset of a robot navigating through a forest, performing predictions in trajectories unseen during training. We try different modality combinations at test time and show that, while our model performs best when all modalities are present, it is still able to perform better than the baseline even when receiving only raw vision input and no proprioception, as well as when only receiving proprioception. Overall, our study demonstrates the importance of leveraging multiple sensors when doing dynamics modeling in outdoor conditions.

2021-06-04

2021 IEEE International Conference on Robotics and Automation (ICRA) (publié)

Encoder-Decoder Neural Architecture Optimization for Keyword Spotting

Tong Mo

Bang Liu

2021-06-03

ArXiv (prépublication)

Hierarchical Video Generation for Complex Data

Lluis Castrejon

Nicolas Ballas

Aaron Courville

2021-06-03

ArXiv (prépublication)

Jean-christophe Gagnon-audet

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization

Soroosh Shahtalebi

A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data d… (voir plus)istribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} across different domains can be seen as finding local (global) minimas in the loss landscape that hold true across all of the training domains. In this paper, we propose a masking strategy, which determines a continuous weight based on the agreement of gradients that flow in each edge of network, in order to control the amount of update received by the edge in each step of optimization. Particularly, our proposed technique referred to as"Smoothed-AND (SAND)-masking", not only validates the agreement in the direction of gradients but also promotes the agreement among their magnitudes to further ensure the discovery of invariances across training domains. SAND-mask is validated over the Domainbed benchmark for domain generalization and significantly improves the state-of-the-art accuracy on the Colored MNIST dataset while providing competitive results on other domain generalization datasets.

2021-06-03

ArXiv (prépublication)

Continual Learning in Deep Networks: an Analysis of the Last Layer

Timothee LESORT

Thomas George

We study how different output layers in a deep neural network learn and forget in continual learning settings. The following three factors… (voir plus) can affect catastrophic forgetting in the output layer: (1) weights modifications, (2) interference, and (3) projection drift. In this paper, our goal is to provide more insights into how changing the output layers may address (1) and (2). Some potential solutions to those issues are proposed and evaluated here in several continual learning scenarios. We show that the best-performing type of the output layer depends on the data distribution drifts and/or the amount of data available. In particular, in some cases where a standard linear layer would fail, it turns out that changing parameterization is sufficient in order to achieve a significantly better performance, whithout introducing a continual-learning algorithm and instead using the standard SGD to train a model. Our analysis and results shed light on the dynamics of the output layer in continual learning scenarios, and suggest a way of selecting the best type of output layer for a given scenario.

2021-06-02

ArXiv (prépublication)

openreview.net

Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion

Suyuchen Wang

Ruihui Zhao

X. T. Chen

Yefeng Zheng

Bang Liu

Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence. The taxonomy expansion task aims … (voir plus)to find a position for a new term in an existing taxonomy to capture the emerging knowledge in the world and keep the taxonomy dynamically updated. Previous taxonomy expansion solutions neglect valuable information brought by the hierarchical structure and evaluate the correctness of merely an added edge, which downgrade the problem to node-pair scoring or mini-path classification. In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. HEF makes use of taxonomy’s hierarchical structure in multiple aspects: i) HEF utilizes subtrees containing most relevant nodes as self-supervision data for a complete comparison of parental and sibling relations; ii) HEF adopts a coherence modeling module to evaluate the coherence of a taxonomy’s subtree by integrating hypernymy relation detection and several tree-exclusive features; iii) HEF introduces the Fitting Score for position selection, which explicitly evaluates both path and level selections and takes full advantage of parental relations to interchange information for disambiguation and self-correction. Extensive experiments show that by better exploiting the hierarchical structure and optimizing taxonomy’s coherence, HEF vastly surpasses the prior state-of-the-art on three benchmark datasets by an average improvement of 46.7% in accuracy and 32.3% in mean reciprocal rank.

2021-06-02

Proceedings of the Web Conference 2021 (publié)

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Rémi Gau

Stephanie Noble

Katja Heuer

Katherine L. Bottenhorn

Isil P. Bilgin

Yu-Fang Yang

Julia M. Huntenburg

Johanna M.M. Bayer

Richard A.I. Bethlehem

Shawn A. Rhoads

Christoph Vogelbacher

Valentina Borghesani

Elizabeth Levitis

Hao-Ting Wang

Sofie Van Den Bossche

Xenia Kobeleva

Jon Haitz Legarreta

Samuel Guay

Selim Melvin Atay

Gael P. Varoquaux … (voir 199 de plus)

Dorien C. Huijser

Malin S. Sandström

Peer Herholz

Samuel A. Nastase

AmanPreet Badhwar

Simon Schwab

Stefano Moia

Michael Dayan

Yasmine Bassil

Paula P. Brooks

Matteo Mancini

James M. Shine

David O'Connor

Xihe Xie

Davide Poggiali

Patrick Friedrich

Anibal S. Heinsfeld

Lydia Riedl

Roberto Toro

César Caballero-Gaudes

Anders Eklund

Kelly G. Garner

Christopher R. Nolan

Damion V. Demeter

Fernando A. Barrios

Junaid S. Merchant

Elizabeth A. McDevitt

Robert Oostenveld

R. Cameron Craddock

Ariel Rokem

Andrew Doyle

Satrajit S. Ghosh

Aki Nikolaidis

Olivia W. Stanley

Eneko Uruñuela

James M. Shine

Aurina Arnatkeviciute

AmanPreet Badhwar

Kelly G. Garner

Ruggero Basanisi

Arshitha Basavaraj

Matteo Mancini

Lune P Bellec

R. Austin Benn

Katherine L. Bottenhorn

Steffen Bollmann

Saskia Bollmann

Andrew Doyle

Jesse Brown

Augusto Buchweitz

Patrick Friedrich

Michael Dayan

Bramsh Q. Chandio

Theresa Cheng

Shawn A. Rhoads

Junaid S. Merchant

Thomas G. Close

Etienne Combrisson

Giorgia Cona

R. Todd Constable

Claire Cury

Kamalaker Dadi

Pablo F. Damasceno

Yasmine Bassil

Fabrizio De Vico Fallani

Krista DeStasio

Erin W. Dickie

Lena Dorfschmidt

Eugene P. Duff

Elizabeth Levitis

Sarah Dziura

Katja Heuer

Oscar Esteban

Shreyas Fadnavis

Jessica E. Flannery

John Flournoy

Stephanie Noble

Alexandre R. Franco

Saampras Ganesan

Yu-Fang Yang

José C. García Alanis

Eleftherios Garyfallidis

Tristan Glatard

Enrico Glerean

Javier Gonzalez-Castillo

Cassandra D. Gould van Praag

Anibal S. Heinsfeld

Geetika Gupta

Katherine L. Bottenhorn

Yaroslav O. Halchenko

Ariel Rokem

Thomas S. Hartmann

Valérie Hayot-Sasson

Stephanie Noble

Felix Hoffstaedter

Daniela M. Hohmann

Corey Horien

Horea-Ioan Ioanas

Alexandru Iordan

Hao-Ting Wang

Michael Dayan

Yasmine Bassil

Agah Karakuzu

David O'Connor

Xenia Kobeleva

Valentina Borghesani

Gregory Kiar

P. Christiaan Klink

Vincent Koppelmans

Serge Koudoro

Angela R. Laird

Georg Langs

Marissa Laws

Roxane Licandro

Sook-Lei Liew

Tomislav Lipic

Elizabeth Levitis

Ariel Rokem

Désirée Lussier

Christopher R. Nolan

Lea-Theresa Mais

Sina Mansour L

J.P. Manzano-Patron

Dimitra Maoutsa

Matteo Mancini

Daniel S. Margulies

Giorgio Marinato

Daniele Marinazzo

Christopher R. Nolan

Camille Maumet

Felipe Meneguzzi

David O'Connor

Michael Dayan

Kathryn L. Mills

Davide Poggiali

Clara A. Moreau

Aysha Motala

Iska Moxon-Emre

Stephanie Noble

Dylan M. Nielson

Gustav Nilsonne

Lydia Riedl

Caroline O’Brien

Emily Olafson

Lindsay D. Oliver

John A. Onofrey

Shawn A. Rhoads

Kendra Oudyk

Patrick Friedrich

Mahboobeh Parsapoor

Lorenzo Pasquini

Scott Peltier

Cyril R. Pernet

Rudolph Pienaar

Pedro Pinheiro-Chagas

Jean-Baptiste Poline

Anqi Qiu

Tiago Quendera

Lydia Riedl

Joscelin Rocha-Hidalgo

Saige Rutherford

Mathias Scharinger

Dustin Scheinost

Deena Shariq

Thomas B. Shaw

Olivia W. Stanley

Molly Simmonite

Nikoloz Sirmpilatze

Hayli Spence

Julia M. Huntenburg

Andrija Stajduhar

Malin S. Sandström

Sylvain Takerkart

Samuel A. Nastase

Link Tejavibulya

Michel Thiebaut de Schotten

Ina Thome

Laura Tomaz da Silva

Nicolas Traut

Lucina Q. Uddin

Antonino Vallesi

Damion V. Demeter

Nandita Vijayakumar

Matteo Visconti di Oleggio Castello

Jakub Vohryzek

Jakša Vukojević

Kirstie Jane Whitaker

Lucy Whitmore

Steve Wideman

Suzanne T. Witt

Xihe Xie

Ting Xu

Michael Dayan

Yu-Fang Yang

B.T. Thomas Yeo

Xi-Nian Zuo

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Depa… (voir plus)rting from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for individual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.

2021-05-31

Neuron (publié)