Portrait de Emmanuel Bengio

Emmanuel Bengio

Membre industriel associé
Scientifique en apprentissage automatique, Recursion
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
GFlowNets
Modèles génératifs
Modélisation moléculaire

Biographie

Emmanuel Bengio est chercheur en ML à Valence Labs/Recursion, où il travaille sur l'intersection des GFlowNets et de la découverte de médicaments. Il a fait son doctorat sous la direction de Joelle Pineau et Doina Precup à McGill/Mila - Institut québécois d'intelligence artificielle, en se concentrant sur la compréhension de la généralisation dans la RL profonde.

Publications

Local Search GFlowNets
Minsu Kim
Taeyoung Yun
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2024-01-16
ICLR.cc/2024/Conference (spotlight)
openreview.net
openreview.net
Maximum entropy GFlowNets with soft Q-learning
Sobhan Mohammadpour
Emmanuel Bengio
Emma Frejinger
Pierre-Luc Bacon
2024-01-01
AISTATS (publié)
doi.org
arxiv.org
SynFlowNet: Towards Molecule Design with Guaranteed Synthesis Pathways
M. Cretu
Charles Harris
Julien Roy
Emmanuel Bengio
Pietro Lio
2024-01-01
arXiv.org (prépublication)
doi.org
SynFlowNet: Towards Molecule Design with Guaranteed Synthesis Pathways
M. Cretu
Charles Harris
Julien Roy
Emmanuel Bengio
Pietro Lio
2024-01-01
arXiv.org (prépublication)
doi.org
A community effort in SARS-CoV-2 drug discovery.
Johannes Schimunek
Philipp Seidl
Katarina Elez
Tim Hempel
Tuan Le
Frank Noé
Simon Olsson
Lluís Raich
Robin Winter
Hatice Gokcan
Filipp Gusev
Evgeny M. Gutkin
Olexandr Isayev
Maria G. Kurnikova
Chamali H. Narangoda
Roman Zubatyuk
Ivan P. Bosko
Konstantin V. Furs
Anna D. Karpenko
Yury V. Kornoushenko … (voir 133 de plus)
Mikita Shuldau
Artsemi Yushkevich
Mohammed B. Benabderrahmane
Patrick Bousquet‐Melou
Ronan Bureau
Beatrice Charton
Bertrand C. Cirou
Gérard Gil
William J. Allen
Suman Sirimulla
Stanley Watowich
Nick Antonopoulos
Nikolaos Epitropakis
Agamemnon Krasoulis
Vassilis Pitsikalis
Stavros Theodorakis
Igor Kozlovskii
Anton Maliutin
Alexander Medvedev
Petr Popov
Mark Zaretckii
Hamid Eghbal‐Zadeh
Christina Halmich
Sepp Hochreiter
Andreas Mayr
Peter Ruch
Michael Widrich
Francois Berenger
Ashutosh Kumar
Yoshihiro Yamanishi
Kam Y. J. Zhang
Emmanuel Bengio
Yoshua Bengio
Moksh J. Jain
Maksym Korablyov
Cheng-Hao Liu
Gilles Marcou
Marcous Gilles
Enrico Glaab
Kelly Barnsley
Suhasini M. Iyengar
Mary Jo Ondrechen
V. Joachim Haupt
Florian Kaiser
Michael Schroeder
Luisa Pugliese
Simone Albani
Christina Athanasiou
Andrea Beccari
Paolo Carloni
Giulia D'Arrigo
Eleonora Gianquinto
Jonas Goßen
Anton Hanke
Benjamin P. Joseph
Daria B. Kokh
Sandra Kovachka
Candida Manelfi
Goutam Mukherjee
Abraham Muñiz‐Chicharro
Francesco Musiani
Ariane Nunes‐Alves
Giulia Paiardi
Giulia Rossetti
S. Kashif Sadiq
Francesca Spyrakis
Carmine Talarico
Alexandros Tsengenes
Rebecca C. Wade
Conner Copeland
Jeremiah Gaiser
Daniel R. Olson
Amitava Roy
Vishwesh Venkatraman
Travis J. Wheeler
Haribabu Arthanari
Klara Blaschitz
Marco Cespugli
Vedat Durmaz
Konstantin Fackeldey
Patrick D. Fischer
Christoph Gorgulla
Christian Gruber
Karl Gruber
Michael Hetmann
Jamie E. Kinney
Krishna M. Padmanabha Das
Shreya Pandita
Amit Singh
Georg Steinkellner
Guilhem Tesseyre
Gerhard Wagner
Zi‐Fu Wang
Ryan J. Yust
Dmitry S. Druzhilovskiy
Dmitry A. Filimonov
Pavel V. Pogodin
Vladimir Poroikov
Anastassia V. Rudik
Leonid A. Stolbov
Alexander V. Veselovsky
Maria De Rosa
Giada De Simone
Maria R. Gulotta
Jessica Lombino
Nedra Mekni
Ugo Perricone
Arturo Casini
Amanda Embree
D. Benjamin Gordon
David Lei
Katelin Pratt
Christopher A. Voigt
Kuang‐Yu Chen
Yves Jacob
Tim Krischuns
Pierre Lafaye
Agnès Zettor
M. Luis Rodríguez
Kris M. White
Daren Fearon
Frank Von Delft
Martin A. Walsh
Dragos Horvath
Charles L. Brooks
Babak Falsafi
Bryan Ford
Adolfo García‐Sastre
Sang Yup Lee
Nadia Naffakh
Alexandre Varnek
Günter Klambauer
Thomas M. Hermans
The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availabili… (voir plus)ty of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.
2023-11-14
Molecular informatics (publié)
doi.org
Baking Symmetry into GFlowNets
George Ma
Emmanuel Bengio
Yoshua Bengio
Dinghuai Zhang
GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. These networks generate objects increment… (voir plus)ally and aim to learn a policy that assigns probability of sampling objects in proportion to rewards. However, the current training pipelines of GFlowNets do not consider the presence of isomorphic actions, which are actions resulting in symmetric or isomorphic states. This lack of symmetry increases the amount of samples required for training GFlowNets and can result in inefficient and potentially incorrect flow functions. As a consequence, the reward and diversity of the generated objects decrease. In this study, our objective is to integrate symmetries into GFlowNets by identifying equivalent actions during the generation process. Experimental results using synthetic data demonstrate the promising performance of our proposed approaches.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Science (présentation orale)
openreview.net
openreview.net
Baking Symmetry into GFlowNets
George Ma
Emmanuel Bengio
Yoshua Bengio
Dinghuai Zhang
GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. These networks generate objects increment… (voir plus)ally and aim to learn a policy that assigns probability of sampling objects in proportion to rewards. However, the current training pipelines of GFlowNets do not consider the presence of isomorphic actions, which are actions resulting in symmetric or isomorphic states. This lack of symmetry increases the amount of samples required for training GFlowNets and can result in inefficient and potentially incorrect flow functions. As a consequence, the reward and diversity of the generated objects decrease. In this study, our objective is to integrate symmetries into GFlowNets by identifying equivalent actions during the generation process. Experimental results using synthetic data demonstrate the promising performance of our proposed approaches.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Science (présentation orale)
doi.org
openreview.net
DGFN: Double Generative Flow Networks
Elaine Lau
Nikhil Murali Vemgal
Doina Precup
Emmanuel Bengio
2023-10-27
NeurIPS.cc/2023/Workshop/GenBio (poster)
doi.org
openreview.net
Local Search GFlowNets
Minsu Kim
Taeyoung Yun
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung Yun
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung Yun
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung Yun
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org