Portrait de Sarath Chandar

Sarath Chandar

Membre académique principal
Chaire en IA Canada-CIFAR
Professeur associé, Polytechnique Montréal, Département d'informatique et de génie logiciel
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Indian Institute of Technology Madras
Sujets de recherche
Alignement de l'IA
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage en ligne
Apprentissage par renforcement
Apprentissage par transfert
Apprentissage profond
Apprentissage tout au long de la vie
Grands modèles de langage (LLM)
IA digne de confiance
Interprétabilité
Modèles de fondation
Optimisation
Réseaux de neurones récurrents
Systèmes multi-agents
Traitement du langage naturel
XAI (IA explicable)

Biographie

Sarath Chandar est professeur associé au départment de génie informatique et génie logiciel de Polytechnique Montréal, où il dirige le laboratoire de recherche Chandar. Il est également membre académique principal à Mila – Institut québécois d’intelligence artificielle, et titulaire d'une chaire en IA Canada-CIFAR et d'une Chaire de recherche du Canada en apprentissage machine permanent.

Ses recherches portent sur l'apprentissage tout au long de la vie, l'apprentissage profond, l'optimisation, l'apprentissage par renforcement et le traitement du langage naturel. Pour promouvoir la recherche sur l'apprentissage tout au long de la vie, Sarath Chandar a créé la Conférence sur les agents d'apprentissage tout au long de la vie (CoLLAs) en 2022 et a présidé le programme en 2022 et en 2023. Il est titulaire d'un doctorat de l'Université de Montréal et d'une maîtrise en recherche de l'Indian Institute of Technology Madras.

Étudiants actuels

Maîtrise recherche - UdeM
Stagiaire de recherche - Polytechnique
Doctorat - Polytechnique
Co-superviseur⋅e :
Stagiaire de recherche - Polytechnique
Collaborateur·rice de recherche
Maîtrise recherche - Polytechnique
Doctorat - Polytechnique
Superviseur⋅e principal⋅e :
Doctorat - Polytechnique
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Collaborateur·rice de recherche - UdeM
Superviseur⋅e principal⋅e :
Doctorat - UdeM
Maîtrise recherche - Polytechnique
Doctorat - Polytechnique
Postdoctorat - Polytechnique
Superviseur⋅e principal⋅e :
Stagiaire de recherche - Polytechnique
Doctorat - UdeM
Doctorat - UdeM
Visiteur de recherche indépendant
Maîtrise recherche - UdeM
Maîtrise recherche - Polytechnique
Maîtrise recherche - UdeM
Doctorat - Polytechnique
Stagiaire de recherche - Polytechnique
Doctorat - Polytechnique
Doctorat - Polytechnique
Doctorat - Polytechnique

Publications

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (voir 13 de plus)
Marc Szafraniec
K. Krishnakumar
Yong Li
Xiaodong Ma
Franziska Meier
Yann LeCun
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (voir plus)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mahmoud Assran
Adrien Bardes
David Fan
Quentin Garrido
Russell Howes
Mojtaba Komeili
Matthew J. Muckley
Ammar Rizvi
Claire Roberts
Koustuv Sinha
Artem Zholus
Sergio Arnaud
Abha Gejji
Ada Martin
Francois Robert Hogan
Daniel Dugas
Piotr Bojanowski
Vasil Khalidov
Patrick Labatut
Francisco Massa … (voir 13 de plus)
Marc Szafraniec
K. Krishnakumar
Yong Li
Xiaodong Ma
Franziska Meier
Yann LeCun
Nicolas Ballas
Fair at Meta
Mila - Québec
AI Institute
Polytechnique Montréal
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supe… (voir plus)rvised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in the physical world. We first pre-train an action-free joint-embedding-predictive architecture, V-JEPA 2, on a video and image dataset comprising over 1 million hours of internet video. V-JEPA 2 achieves strong performance on motion understanding (77.3 top-1 accuracy on Something-Something v2) and state-of-the-art performance on human action anticipation (39.7 recall-at-5 on Epic-Kitchens-100) surpassing previous task-specific models. Additionally, after aligning V-JEPA 2 with a large language model, we demonstrate state-of-the-art performance on multiple video question-answering tasks at the 8 billion parameter scale (e.g., 84.0 on PerceptionTest, 76.9 on TempCompass). Finally, we show how self-supervised learning can be applied to robotic planning tasks by post-training a latent action-conditioned world model, V-JEPA 2-AC, using less than 62 hours of unlabeled robot videos from the Droid dataset. We deploy V-JEPA 2-AC zero-shot on Franka arms in two different labs and enable picking and placing of objects using planning with image goals. Notably, this is achieved without collecting any data from the robots in these environments, and without any task-specific training or reward. This work demonstrates how self-supervised learning from web-scale data and a small amount of robot interaction data can yield a world model capable of planning in the physical world.
Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
Milan Bhan
Jean-Noël Vittaut
Nicolas Chesneau
Marie-Jeanne Lesot
Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify… (voir plus) their answers. Despite their logical appearance, self-NLE do not necessarily reflect the LLM actual decision-making process, making such explanations unfaithful. While existing methods for measuring self-NLE faithfulness mostly rely on behavioral tests or computational block identification, none of them examines the neural activity underlying the model's reasoning. This work introduces a novel flexible framework for quantitatively measuring the faithfulness of LLM-generated self-NLE by directly comparing the latter with interpretations of the model's internal hidden states. The proposed framework is versatile and provides deep insights into self-NLE faithfulness by establishing a direct connection between self-NLE and model reasoning. This approach advances the understanding of self-NLE faithfulness and provides building blocks for generating more faithful self-NLE.
Revisiting the Goldilocks Zone in Inhomogeneous Networks
Zacharie Garnier Cuchet
Ekaterina Lobacheva
We investigate how architectural inhomogeneities—such as biases, layer normalization, and residual connections—affect the curvature of t… (voir plus)he loss landscape at initialization and its link to trainability. We focus on the Goldilocks zone, a region in parameter space with excess positive curvature, previously associated with improved optimization in homogeneous networks. To extend this analysis, we compare two scaling strategies: weight scaling and softmax temperature scaling. Our results show that in networks with biases or residual connections, both strategies identify a Goldilocks zone aligned with better training. In contrast, layer normalization leads to lower or negative curvature, yet stable optimization—revealing a disconnect between curvature and trainability. Softmax temperature scaling behaves more consistently across models, making it a more robust probe. Overall, the Goldilocks zone remains relevant in inhomogeneous networks, but its geometry and predictive power depend on architectural choices, particularly normalization.
Boosting LLM Reasoning via Spontaneous Self-Correction
Xutong Zhao
Tengyu Xu
Xuewei Wang
Zhengxing Chen
Di Jin
Liang Tan
Zishun Yu
Zhuokai Zhao
Yun He
Sinong Wang
Si-Yuan Wang
Han Fang
Chen Zhu
MetaAI
Mila - Québec
AI Institute
Polytechnique Montréal
While large language models (LLMs) have demonstrated remarkable success on a broad range of tasks, math reasoning remains a challenging one.… (voir plus) One of the approaches for improving math reasoning is self-correction, which designs self-improving loops to let the model correct its own mistakes. However, existing self-correction approaches treat corrections as standalone post-generation refinements, relying on extra prompt and system designs to elicit self-corrections, instead of performing real-time, spontaneous self-corrections in a single pass. To address this, we propose SPOC, a spontaneous self-correction approach that enables LLMs to generate interleaved solutions and verifications in a single inference pass, with generation dynamically terminated based on verification outcomes, thereby effectively scaling inference time compute. SPOC considers a multi-agent perspective by assigning dual roles -- solution proposer and verifier -- to the same model. We adopt a simple yet effective approach to generate synthetic data for fine-tuning, enabling the model to develop capabilities for self-verification and multi-agent collaboration. We further improve its solution proposal and verification accuracy through online reinforcement learning. Experiments on mathematical reasoning benchmarks show that SPOC significantly improves performance. Notably, SPOC boosts the accuracy of Llama-3.1-8B and 70B Instruct models, achieving gains of 8.8% and 11.6% on MATH500, 10.0% and 20.0% on AMC23, and 3.3% and 6.7% on AIME24, respectively.
Boosting LLM Reasoning via Spontaneous Self-Correction
Xutong Zhao
Tengyu Xu
Xuewei Wang
Zhengxing Chen
Di Jin
Liang Tan
Zishun Yu
Zhuokai Zhao
Yun He
Sinong Wang
Han Fang
Chen Zhu
MetaAI
Mila - Québec
AI Institute
Polytechnique Montréal
While large language models (LLMs) have demonstrated remarkable success on a broad range of tasks, math reasoning remains a challenging one.… (voir plus) One of the approaches for improving math reasoning is self-correction, which designs self-improving loops to let the model correct its own mistakes. However, existing self-correction approaches treat corrections as standalone post-generation refinements, relying on extra prompt and system designs to elicit self-corrections, instead of performing real-time, spontaneous self-corrections in a single pass. To address this, we propose SPOC, a spontaneous self-correction approach that enables LLMs to generate interleaved solutions and verifications in a single inference pass, with generation dynamically terminated based on verification outcomes, thereby effectively scaling inference time compute. SPOC considers a multi-agent perspective by assigning dual roles -- solution proposer and verifier -- to the same model. We adopt a simple yet effective approach to generate synthetic data for fine-tuning, enabling the model to develop capabilities for self-verification and multi-agent collaboration. We further improve its solution proposal and verification accuracy through online reinforcement learning. Experiments on mathematical reasoning benchmarks show that SPOC significantly improves performance. Notably, SPOC boosts the accuracy of Llama-3.1-8B and 70B Instruct models, achieving gains of 8.8% and 11.6% on MATH500, 10.0% and 20.0% on AMC23, and 3.3% and 6.7% on AIME24, respectively.
Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
Enamundram Naga Karthik
Sandrine B'edard
Jan Valovsek
Christoph Aigner
Elise Bannier
Josef Bednavr'ik
Virginie Callot
Anna Combes
Armin Curt
Gergely David
Falk Eippert
Lynn Farner
M. G. Fehlings
Patrick Freund
Tobias Granberg
Cristina Granziera
Rhscir Network Imaging Group
Ulrike Horn
Tom'avs Hor'ak
Suzanne Humphreys … (voir 36 de plus)
Markus Hupp
Anne Kerbrat
Nawal Kinany
Shannon Kolind
Petr Kudlivcka
Anna Lebret
Lisa Eunyoung Lee
Caterina Mainero
Allan R. Martin
Megan McGrath
Govind Nair
Kristin P. O’Grady
Jiwon Oh
Russell Ouellette
Nikolai Pfender
Dario Pfyffer
P. Pradat
Alexandre Prat
Emanuele Pravatà
D. S. Reich
Ilaria Ricchi
Naama Rotem-Kohavi
Simon Schading-Sassenhausen
Maryam Seif
Andrew C. Smith
Seth Aaron Smith
Grace Sweeney
Roger Tam
Anthony Traboulsee
Constantina A. Treaba
Charidimos Tsagkas
Zachary Vavasour
Dimitri Van De Ville
Kenneth A. Weber
Monitoring morphometric drift in lifelong learning segmentation of the spinal cord
Enamundram Naga Karthik
Sandrine B'edard
Jan Valovsek
Christoph Aigner
Elise Bannier
Josef Bednavr'ik
Virginie Callot
Anna Combes
Armin Curt
Gergely David
Falk Eippert
Lynn Farner
M. G. Fehlings
Patrick Freund
Tobias Granberg
Cristina Granziera
Rhscir Network Imaging Group
Ulrike Horn
Tom'avs Hor'ak
Suzanne Humphreys … (voir 36 de plus)
Markus Hupp
Anne Kerbrat
Nawal Kinany
Shannon Kolind
Petr Kudlivcka
Anna Lebret
L. Lee
Caterina Mainero
Allan R. Martin
Megan McGrath
Govind Nair
Kristin P. O’Grady
Jiwon Oh
Russell Ouellette
Nikolai Pfender
Dario Pfyffer
P. Pradat
Alexandre Prat
Emanuele Pravatà
D. S. Reich
Ilaria Ricchi
Naama Rotem-Kohavi
Simon Schading-Sassenhausen
Maryam Seif
Andrew C. Smith
Seth Aaron Smith
Grace Sweeney
Roger Tam
Anthony Traboulsee
Constantina A. Treaba
Charidimos Tsagkas
Zachary Vavasour
Dimitri Van De Ville
Kenneth A. Weber
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus
Maksim Kuznetsov
Roman Schutski
Shayakhmetov Rim
Daniil Polykovskiy
Alex Zhavoronkov
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding … (voir plus)of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample.
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus
Carl Doersch
Yi Yang
Skanda Koppula
Viorica Patraucean
Xu Owen He
Ignacio Rocco
Mehdi S. M. Sajjadi
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus
Carl Doersch
Yi Yang
Skanda Koppula
Viorica Patraucean
Xu Owen He
Ignacio Rocco
Mehdi S. M. Sajjadi
CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning
Prashant Govindarajan
Mathieu Reymond
Antoine Clavaud
Mariano Phielipp
Santiago Miret
*In silico* design and optimization of new materials primarily relies on high-accuracy atomic simulators that perform density functional the… (voir plus)ory (DFT) calculations. While recent works showcase the strong potential of machine learning to accelerate the material design process, they mostly consist of generative approaches that do not use direct DFT signals as feedback to improve training and generation mainly due to DFT's high computational cost. To aid the adoption of direct DFT signals in the materials design loop through online reinforcement learning (RL), we propose **CrystalGym**, an open-source RL environment for crystalline material discovery. Using CrystalGym, we benchmark value- and policy-based reinforcement learning algorithms for designing various crystals conditioned on target properties. Concretely, we optimize for challenging properties like the band gap, bulk modulus, and density, which are directly calculated from DFT in the environment. While none of the algorithms we benchmark solve all CrystalGym tasks, our extensive experiments and ablations show different sample efficiencies and ease of convergence to optimality for different algorithms and environment settings. Our goal is for CrystalGym to serve as a test bed for reinforcement learning researchers and material scientists to address these real-world design problems with practical applications. Furthermore, we introduce a novel class of challenges for reinforcement learning methods dealing with time-consuming reward signals, paving the way for future interdisciplinary research for machine learning motivated by real-world applications.