Sarath Chandar

Biography

Sarath Chandar is an associate professor at Polytechnique Montreal's Department of Computer and Software Engineering, where he leads the Chandar Research Lab. He is also a Core Academic Member at Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair and the Canada Research Chair in Lifelong Machine Learning.

Chandar’s research interests include lifelong learning, deep learning, optimization, reinforcement learning and natural language processing. To promote research in lifelong learning, Chandar created the Conference on Lifelong Learning Agents (CoLLAs) in 2022, for which he served as program chair in 2022 and 2023.

He has a PhD from Université de Montréal and an MSc (By Research) from the Indian Institute of Technology Madras.

Current Students

Ista Abbes

Master's Research - Université de Montréal

Davide Baldelli

PhD - Polytechnique Montréal

Co-supervisor :

Master's Research - Polytechnique Montréal

Naga Karthik Enamundram

PhD - Polytechnique Montréal

Principal supervisor :

Julien Cohen-Adad

emvnagakarthik@gmail.com

Prashant Govindarajan

PhD - Polytechnique Montréal

Simon Guiroy

PhD - Université de Montréal

Principal supervisor :

Collaborating researcher - Université de Montréal

Principal supervisor :

Liam Paull

Maryam Hashemzadeh

PhD - Université de Montréal

David Heurtel--Depeiges

PhD - Polytechnique Montréal

Amir Ardalan Kalantari Dehaghi

Jerry Huang

PhD - Université de Montréal

Collaborating Alumni

Lola Le Breton

Master's Research - Polytechnique Montréal

Ekaterina Lobacheva

Postdoctorate - Université de Montréal

PhD - Polytechnique Montréal

Roshan Balaji Munirathinam Sankaran Balaji

Mohamed Amine Merzouk

Postdoctorate - Polytechnique Montréal

Principal supervisor :

Research Intern - Polytechnique Montréal

Hadi NekoeiQachkanloo

PhD - Université de Montréal

Darshan Patil

PhD - Université de Montréal

Gabriele Prato

PhD - Université de Montréal

Postdoctorate

Independent visiting researcher

Mohammad R. Samsami

Master's Research - Université de Montréal

Master's Research - Polytechnique Montréal

Arjun Vaithilingam Sudhakar

Megh Thakkar

Master's Research - Université de Montréal

PhD - Polytechnique Montréal

Kowen Woo

Research Intern - Polytechnique Montréal

Abdelrahman Zayed

PhD - Polytechnique Montréal

Xutong Zhao

PhD - Polytechnique Montréal

Artem Zholus

PhD - Polytechnique Montréal

NeoBERT: A New Frontier for Open-Source Encoder Language Models

Blog Posts

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

March 3, 2025

Lola Le Breton

Quentin Fournier

Sarath Chandar

Read the article

October 1, 2024

How Do We Explain AI and Ensure the Explanation Is True? Faithfulness Measurable Models Tell You How

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

Contrast-agnostic Spinal Cord Segmentation: A Comparative Study of ConvNets and Vision Transformers

Enamundram Naga Karthik

Sandrine Bédard

Jan Valošek

Julien Cohen-Adad

The cross-sectional area (CSA) of the spinal cord (SC) computed from its segmentation is a relevant clinical biomarker for the diagnosis and… (see more) monitoring of cord compression and atrophy. One key limitation of existing automatic methods is that their SC segmentations depend on the MRI contrast, resulting in different CSA across contrasts. Furthermore, these methods rely on CNNs, leaving a gap in the literature for exploring the performance of modern deep learning (DL) architectures. In this study, we extend our recent work \cite{Bdard2023TowardsCS} by evaluating the contrast-agnostic SC segmentation capabilities of different classes of DL architectures, namely, ConvNeXt, vision transformers (ViTs), and hierarchical ViTs. We compared 7 different DL models using the open-source \textit{Spine Generic} Database of healthy participants

2024-04-27

MIDL.io/2024/Short_Papers (accepted)

Towards Practical Tool Usage for Continually Learning LLMs

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

Large language models (LLMs) show an innate skill for solving language based tasks. But insights have suggested an inability to adjust for i… (see more)nformation or task-solving skills becoming outdated, as their knowledge, stored directly within their parameters, remains static in time. Tool use helps by offloading work to systems that the LLM can access through an interface, but LLMs that use them still must adapt to nonstationary environments for prolonged use, as new tools can emerge and existing tools can change. Nevertheless, tools require less specialized knowledge, therefore we hypothesize they are better suited for continual learning (CL) as they rely less on parametric memory for solving tasks and instead focus on learning when to apply pre-defined tools. To verify this, we develop a synthetic benchmark and follow this by aggregating existing NLP tasks to form a more realistic testing scenario. While we demonstrate scaling model size is not a solution, regardless of tool usage, continual learning techniques can enable tool LLMs to both adapt faster while forgetting less, highlighting their potential as continual learners.

2024-04-14

ArXiv (preprint)

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… (see more)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.

2024-03-07

ArXiv (preprint)

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resett… (see more)ing} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.

2024-01-16

ICLR.cc/2024/Conference (poster)

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

2024-01-16

ICLR.cc/2024/Conference (poster)

Mastering Memory Tasks with World Models

Mohammad Reza Samsami

Artem Zholus

Janarthanan Rajendran

2024-01-16

ICLR.cc/2024/Conference (oral)

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

Jerry Huang

Prasanna Parthasarathi

Mehdi Rezagholizadeh

2024-01-01

EMNLP (published)

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… (see more)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) (published)

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… (see more)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 (published)

www.semanticscholar.org

Do Large Language Models Know How Much They Know?

Gabriele Prato

Jerry Huang

Prasanna Parthasarathi

Shagun Sodhani

2024-01-01

EMNLP (published)

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… (see more)covery. Recent developments in generative and geometric deep learning have shown...

2024-01-01

Digital Discovery (published)

Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning

Prashant Govindarajan

Santiago Miret

Jarrid Rector-Brooks

Mariano Phielipp

Janarthanan Rajendran

2024-01-01

Digital Discovery (published)