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 :

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 Balaji 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

Environments for Lifelong Reinforcement Learning

Khimya Khetarpal

Shagun Sodhani

Doina Precup

To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific ta… (voir plus)sk but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.

2018-11-26

ArXiv (prépublication)

On Training Recurrent Neural Networks for Lifelong Learning

Shagun Sodhani

Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we stud… (voir plus)y these challenges in the context of sequential supervised learning with emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step towards developing true lifelong learning systems, we unify Gradient Episodic Memory (a catastrophic forgetting alleviation approach) and Net2Net(a capacity expansion approach). Both these models are proposed in the context of feedforward networks and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.

2018-11-16

ArXiv (prépublication)

Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes

Caglar Gulcehre

Kyunghyun Cho

We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This … (voir plus)addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies, including both linear and nonlinear ones. We implement the D-NTM with both continuous and discrete read and write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU controller. We provide extensive analysis of our model and compare different variations of neural Turing machines on this task. We show that our model outperforms long short-term memory and NTM variants. We provide further experimental results on the sequential MNIST, Stanford Natural Language Inference, associative recall, and copy tasks.

2018-03-20

Neural Computation (publié)

doi.org

openreview.net

A Deep Reinforcement Learning Chatbot (Short Version)

Iulian V. Serban

Chinnadhurai Sankar

Mathieu Germain

Saizheng Zhang

Zhouhan Lin

Sandeep Subramanian

Taesup Kim

Michael Pieper

Nan Rosemary Ke

Sai Rajeswar

Alexandre De Brébisson

Jose Sotelo

Dendi Suhubdy

Vincent Michalski

Alexandre Nguyen

Joelle Pineau

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon … (voir plus)Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.

2018-01-20

ArXiv (prépublication)

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

Harm de Vries

Florian Strub

Olivier Pietquin

Hugo Larochelle

Aaron Courville

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The… (voir plus) goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.

2017-07-21

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

doi.org

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Iulian V. Serban

Alberto García-Durán

Caglar Gulcehre

Sungjin Ahn

Aaron Courville

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. Howeve… (voir plus)r, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.

2016-08-01

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

doi.org