Portrait de Sarath Chandar

Sarath Chandar

Membre académique principal
sarath.chandar@mila.quebec
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)
Site web
Google Scholar

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

Istabrak Abbes
Doctorat - UdeM
istabrak.abbes@mila.quebec
Alex Aselstyne
Maîtrise recherche - Polytechnique
alexander.aselstyne@mila.quebec
Site web
Github
Davide Baldelli
Doctorat - Polytechnique
Co-superviseur⋅e :
Amal Zouaq
davide.baldelli@mila.quebec
Site web
Github
Google Scholar
Milan Bhan
Collaborateur·rice de recherche
milan.bhan@mila.quebec
Diego Cerda Mardini
Maîtrise recherche - McGill
diego.cerda-mardini@mila.quebec
Naga Karthik Enamundram
Doctorat - Polytechnique
Superviseur⋅e principal⋅e :
Julien Cohen-Adad
emvnagakarthik@gmail.com
Site web
Github
Google Scholar
Prashant Govindarajan
Doctorat - Polytechnique
prashant.govindarajan@mila.quebec
Site web
Google Scholar
Simon Guiroy
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Chris Pal
guiroys@mila.quebec
Site web
Github
Google Scholar
Kshitij Gupta
Collaborateur·rice de recherche
Superviseur⋅e principal⋅e :
Irina Rish
kshitij.gupta@mila.quebec
Nathanaël Haas
Stagiaire de recherche - Polytechnique
nathanael.haas@mila.quebec
Maryam Hashemzadeh
Doctorat - UdeM
maryam.hashemzadeh@mila.quebec
Site web
Google Scholar
David Heurtel--Depeiges
Doctorat - Polytechnique
david.heurtel-depeiges@mila.quebec
Site web
Github
Google Scholar
Jerry Huang
Doctorat - UdeM
jerry.huang@mila.quebec
Kanishk Jain
Doctorat - UdeM
Co-superviseur⋅e :
Ross Goroshin
kanishk.jain@mila.quebec
Site web
Github
Google Scholar
Saurav Jha
Postdoctorat - Polytechnique
saurav.jha@mila.quebec
Site web
Github
Google Scholar
Amir Ardalan Kalantari Dehaghi
Doctorat - UdeM
kalantaa@mila.quebec
Github
Google Scholar
Antoine Lafrance
Maîtrise recherche - Polytechnique
antoine.lafrance@mila.quebec
Github
LinkedIn
Lola Le Breton
Doctorat - Polytechnique
lola.lebreton@mila.quebec
Site web
Github
Aidan Li
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Ross Goroshin
aidan.li@mila.quebec
Site web
Github
LinkedIn
Google Scholar
Ekaterina Lobacheva
Postdoctorat - UdeM
ekaterina.lobacheva@mila.quebec
Site web
Github
Google Scholar
Pranshu Malviya
Doctorat - Polytechnique
pranshu.malviya@mila.quebec
Site web
Github
Google Scholar
Roshan Munirathinam Sankaran Balaji
Collaborateur·rice de recherche - Polytechnique
roshan.munirathinam@mila.quebec
Site web
Github
Google Scholar
Hadi NekoeiQachkanloo
Doctorat - UdeM
nekoeihe@mila.quebec
Nilaksh Nilaksh
Doctorat - Polytechnique
nilaksh.nilaksh@mila.quebec
Site web
Github
Google Scholar
Darshan Patil
Doctorat - UdeM
darshan.patil@mila.quebec
Site web
Github
Linda Peinthiere
Collaborateur·rice de recherche - Polytechnique Montreal
uphro_1@hotmail.com
Github
Yann Pernot
Maîtrise recherche - Polytechnique
yann.pernot@mila.quebec
Mathieu Reymond
Collaborateur·rice alumni
mathieu.reymond@mila.quebec
Github
Google Scholar
Shaipranesh Senthilkumar
Doctorat - Polytechnique
shaipraneshgci@gmail.com
Google Scholar
Nour Shaheen
Maîtrise recherche - Polytechnique
Superviseur⋅e principal⋅e :
Amine Mhedhbi
nour.shaheen@mila.quebec
Site web
Github
Google Scholar
Nishant Sharma
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Amal Zouaq
nishant.sharma@mila.quebec
Site web
Github
LinkedIn
Achille Sowa Samo
Doctorat - Polytechnique
achille.sowa@mila.quebec
Anabel Tan
Postdoctorat - UdeM
anabel.tan@mila.quebec
Megh Thakkar
Maîtrise recherche - UdeM
megh.thakkar@mila.quebec
Arjun Vaithilingam Sudhakar
Doctorat - Polytechnique
arjun.vaithilingam-sudhakar@mila.quebec
Abdelrahman Zayed
Doctorat - Polytechnique
zayedabd@mila.quebec
Site web
Google Scholar
Xutong Zhao
Doctorat - Polytechnique
xutong.zhao@mila.quebec
Artem Zholus
Doctorat - Polytechnique
artem.zholus@mila.quebec
Site web
Github
Google Scholar

Publications

A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban
Chinnadhurai Sankar
Mathieu Germain
Saizheng Zhang
Zhouhan Lin
Sandeep Subramanian
Taesup Kim
Michael Pieper
Sarath Chandar
Nan Rosemary Ke
Sai Rajeswar
Alexandre De Brébisson
Jose M. R. Sotelo
Dendi Suhubdy
Vincent Michalski
Alexandre Nguyen
Joelle Pineau
Yoshua Bengio
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.
2018-01-19
arXiv (Cornell University) (prépublication)
doi.org
arxiv.org
A Deep Reinforcement Learning Chatbot
Iulian V. Serban
Chinnadhurai Sankar
Mathieu Germain
Saizheng Zhang
Zhouhan Lin
Sandeep Subramanian
Taesup Kim
Michael Pieper
Sarath Chandar
Nan Rosemary Ke
Sai Mudumba
Alexandre De Brébisson
Jose M. R. Sotelo
Dendi Suhubdy
Vincent Michalski
Alexandre Nguyen
Joelle Pineau
Yoshua Bengio
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 template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network 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 many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.
2017-09-06
ArXiv (prépublication)
doi.org
arxiv.org
Hierarchical Memory Networks
Sarath Chandar
Sungjin Ahn
Hugo Larochelle
Pascal Vincent
Gerald Tesauro
Yoshua Bengio
Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is oft… (voir plus)en addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which require the network to read from extremely large memories. On the other hand, it is well known that hard attention mechanisms based on reinforcement learning are challenging to train successfully. In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks. The memory is organized in a hierarchical structure such that reading from it is done with less computation than soft attention over a flat memory, while also being easier to train than hard attention over a flat memory. Specifically, we propose to incorporate Maximum Inner Product Search (MIPS) in the training and inference procedures for our hierarchical memory network. We explore the use of various state-of-the art approximate MIPS techniques and report results on SimpleQuestions, a challenging large scale factoid question answering task.
2015-12-31
CoRR (publié)
doi.org
arxiv.org