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

Davide Baldelli

Doctorat - Polytechnique

Co-superviseur⋅e :

Amal Zouaq

Antoine Clavaud

Maîtrise recherche - Polytechnique

Louis Clouatre

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Doctorat - UdeM

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

Jerry Huang

Doctorat - UdeM

Khurram Javed

Visiteur de recherche indépendant - NA

Amir Ardalan Kalantari Dehaghi

Collaborateur·rice alumni

Lola Le Breton

Maîtrise recherche - Polytechnique

andreas.madsen@mila.quebec

Andreas Madsen

Doctorat - Polytechnique

Co-superviseur⋅e :

Siva Reddy

Doctorat - Polytechnique

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Superviseur⋅e principal⋅e :

Hadi NekoeiQachkanloo

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Ali Rahimi-Kalahroudi

Maîtrise recherche - UdeM

Janarthanan Rajendran

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Postdoctorat

Visiteur de recherche indépendant

Arjun Vaithilingam Sudhakar

Mohammad R. Samsami

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Doctorat - Polytechnique

Nishanth Anand Vemgal

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - Polytechnique

Xutong Zhao

Doctorat - Polytechnique

Artem Zholus

Doctorat - Polytechnique

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

Billets de blogue

1 octobre 2024

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

Mastering Memory Tasks with World Models

Mohammad Reza Samsami

Artem Zholus

Janarthanan Rajendran

Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solv… (voir plus)e tasks involving extended time gaps between actions and outcomes, or tasks demanding the recalling of distant observations to inform current actions. To improve temporal coherence, we integrate a new family of state space models (SSMs) in world models of MBRL agents to present a new method, Recall to Imagine (R2I). This integration aims to enhance both long-term memory and long-horizon credit assignment. Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze. At the same time, it upholds comparable performance in classic RL tasks, such as Atari and DMC, suggesting the generality of our method. We also show that R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster wall-time convergence.

2024-01-16

ICLR.cc/2024/Conference (présentation orale)

openreview.net

Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

2024-01-01

EMNLP (publié)

arxiv.org

Exploring Quantization for Efficient Pre-Training of Transformer Language Models

Kamran Chitsaz

Quentin Fournier

Goncalo Mordido

The increasing scale of Transformer models has led to an increase in their pre-training computational requirements. While quantization has p… (voir plus)roven to be effective after pre-training and during fine-tuning, applying quantization in Transformers during pre-training has remained largely unexplored at scale for language modeling. This study aims to explore the impact of quantization for efficient pre-training of Transformers, with a focus on linear layer components. By systematically applying straightforward linear quantization to weights, activations, gradients, and optimizer states, we assess its effects on model efficiency, stability, and performance during training. By offering a comprehensive recipe of effective quantization strategies to be applied during the pre-training of Transformers, we promote high training efficiency from scratch while retaining language modeling ability. Code is available at https://github.com/chandar-lab/EfficientLLMs.

2024-01-01

EMNLP (Findings) (publié)

arxiv.org

Do Large Language Models Know How Much They Know?

Gabriele Prato

Jerry Huang

Prasanna Parthasarathi

Shagun Sodhani

Large Language Models (LLMs) have emerged as highly capable systems and are increasingly being integrated into various uses. Nevertheless, t… (voir plus)he rapid advancement in their deployment trails a comprehensive understanding of their internal mechanisms, as well as a delineation of their capabilities and limitations. A desired characteristic of an intelligent system is its ability to recognize the scope of its own knowledge. To investigate whether LLMs embody this attribute, we develop a benchmark that challenges these models to enumerate all information they possess on specific topics. This benchmark assesses whether the models recall excessive, insufficient, or the precise amount of required information, thereby indicating their awareness of how much they know about the given topic. Our findings reveal that the emergence of this property varies across different architectures and manifests at diverse rates. However, with sufficient scaling, all tested models are ultimately capable of performing this task. The insights gained from this research advance our understanding of LLMs, shedding light on their operational capabilities and contributing to the ongoing exploration of their intricate dynamics.

2024-01-01

Conference on Empirical Methods in Natural Language Processing (publié)

www.semanticscholar.org

Do Large Language Models Know How Much They Know?

Gabriele Prato

Jerry Huang

Prasanna Parthasarathi

Shagun Sodhani

2024-01-01

EMNLP (publié)

Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

Janarthanan Rajendran

Navigating through the exponentially large chemical space to search for desirable materials is an extremely challenging task in material dis… (voir plus)covery. Recent developments in generative and geometric deep learning have shown...

2024-01-01

Digital Discovery (publié)

openreview.net

MVP: Minimal Viable Phrase for Long Text Understanding.

Louis Clouâtre

Amal Zouaq