Sarath Chandar

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

Ista Abbes

Maîtrise recherche - UdeM

Alex Aselstyne

Stagiaire de recherche - Polytechnique

Davide Baldelli

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - Polytechnique

Naga Karthik Enamundram

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Julien Cohen-Adad

emvnagakarthik@gmail.com

Prashant Govindarajan

Doctorat - Polytechnique

Simon Guiroy

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

David Heurtel--Depeiges

Doctorat - Polytechnique

Amir Ardalan Kalantari Dehaghi

Jerry Huang

Doctorat - UdeM

Collaborateur·rice alumni

Lola Le Breton

Maîtrise recherche - Polytechnique

Postdoctorat - UdeM

Doctorat - Polytechnique

Roshan Munirathinam Sankaran Balaji

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Superviseur⋅e principal⋅e :

Stagiaire de recherche - Polytechnique

Hadi NekoeiQachkanloo

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Postdoctorat

Visiteur de recherche indépendant

Mohammad R. Samsami

Maîtrise recherche - UdeM

Maîtrise recherche - Polytechnique

Arjun Vaithilingam Sudhakar

Megh Thakkar

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Kowen Woo

Stagiaire de recherche - Polytechnique

Abdelrahman Zayed

Doctorat - Polytechnique

Xutong Zhao

Doctorat - Polytechnique

Artem Zholus

Doctorat - Polytechnique

NeoBERT: une nouvelle frontière pour les modèles de langage encodeurs open-source

Billets de blogue

A digital picture of Bert from Sesame street, wering black trench coat and sunglasses

3 mars 2025

par

Lola Le Breton

Quentin Fournier

Sarath Chandar

Lire l'article

1 octobre 2024

Comment expliquer l’IA et s’assurer que cette explication est vraie? Les modèles mesurables de fidélité vous indiquent comment y parvenir

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

NeoBERT: A Next-Generation BERT

Lola Le Breton

Quentin Fournier

John Xavier Morris

Mariam El Mezouar

Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of … (voir plus)large auto-regressive language models such as LLaMA and DeepSeek. In contrast, encoders like BERT and RoBERTa have not seen the same level of progress despite being foundational for many downstream NLP applications. To bridge this gap, we introduce NeoBERT, a next-generation encoder that redefines the capabilities of bidirectional models by integrating state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. NeoBERT is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an optimal depth-to-width ratio, and leverages an extended context length of 4,096 tokens. Despite its compact 250M parameter footprint, it achieves state-of-the-art results on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions. In addition, we rigorously evaluate the impact of each modification on GLUE and design a uniform fine-tuning and evaluation framework for MTEB. We release all code, data, checkpoints, and training scripts to accelerate research and real-world adoption.

2025-06-03

TMLR (accepté)

openreview.net

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

Julien Cohen-Adad

2025-05-02

ArXiv (prépublication)

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

Julien Cohen-Adad

2025-05-02

ArXiv (prépublication)

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.

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

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

Ross Goroshin

2025-04-08

ArXiv (prépublication)

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

Ross Goroshin

2025-04-01

arXiv (publié)

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.

2025-03-03

ICLR.cc/2025/Workshop/AI4MAT (spotlight)

openreview.net

Steering Large Language Model Activations in Sparse Spaces

Reza Bayat

Ali Rahimi-Kalahroudi

Mohammad Pezeshki

Pascal Vincent

A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time. Activation steering, which… (voir plus) modifies internal model activations during inference, offers a potential solution. However, prior work in dense activation spaces struggles with superposition, wherein multiple features become entangled, limiting interpretability and precise control. In contrast, sparse representations provide an untapped opportunity for more interpretable behavior modulation. In this work, we introduce sparse activation steering (SAS), a method that leverages sparse autoencoders (SAEs) to steer LLM behavior in sparse spaces. By isolating behavior-specific features through a contrastive prompt-pairing approach, we define a set of features that can selectively reinforce or suppress behaviors. Experiments on Gemma 2 LLMs show that SAS vectors enable nuanced behavioral modulation and finer-grained control. Furthermore, scaling SAEs improves monosemanticity of SAS vectors, suggesting more reliable and interpretable interventions.

2025-02-28

ArXiv (prépublication)

Steering Large Language Model Activations in Sparse Spaces

Reza Bayat

Ali Rahimi-Kalahroudi

Mohammad Pezeshki

Pascal Vincent

2025-02-28

ArXiv (prépublication)

NeoBERT: A Next-Generation BERT

Lola Le Breton

Quentin Fournier

Mariam El Mezouar

2025-02-26

ArXiv (prépublication)

Sub-goal Distillation: A Method to Improve Small Language Agents

Maryam Hashemzadeh

Elias Stengel-Eskin

Marc-Alexandre Côté

While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational req… (voir plus)uirements and restricted number of calls constrain their practical utility, especially in long-horizon interactive tasks such as decision-making or in scenarios involving continuous ongoing tasks. To address these constraints, we propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model (770M parameters). Our approach involves constructing a hierarchical agent comprising a planning module, which learns through Knowledge Distillation from an LLM to generate sub-goals, and an execution module, which learns to accomplish these sub-goals using elementary actions. In detail, we leverage an LLM to annotate an oracle path with a sequence of sub-goals towards completing a goal. Subsequently, we utilize this annotated data to fine-tune both the planning and execution modules. Importantly, neither module relies on real-time access to an LLM during inference, significantly reducing the overall cost associated with LLM interactions to a fixed cost. In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7% (absolute). Our analysis highlights the efficiency of our approach compared to other LLM-based methods. Our code and annotated data for distillation can be found on GitHub.

2025-02-17

Proceedings of The 3rd Conference on Lifelong Learning Agents (publié)

A Generalist Hanabi Agent

Arjun V Sudhakar

Hadi Nekoei

Mathieu Reymond

Miao Liu

Janarthanan Rajendran