Portrait of Dzmitry Bahdanau

Dzmitry Bahdanau

Core Industry Member
Canada CIFAR AI Chair
Associate Professor, McGill University, School of Computer Science
AI Research Scientist, ServiceNow
Research Topics
Deep Learning
Natural Language Processing
Google Scholar

Biography

Dzmitry Bahdanau is an adjunct professor at McGill University and a research scientist at ServiceNow.

He completed his PhD at Mila – Quebec Artificial Intelligence Institute and Université de Montréal under the direction of Yoshua Bengio.

Bahdanau is interested in fundamental and applied questions concerning natural language understanding. His main research areas include semantic parsing, language user interfaces, systematic generalization and hybrid neural-symbolic systems.

Current Students

Jay Gala
Master's Research - McGill University
Principal supervisor :
Siva Reddy
Website
Google Scholar
Fengyuan Liu
Master's Research - McGill University
Principal supervisor :
Siva Reddy
Website
Github
Arkil Patel
PhD - McGill University
Co-supervisor :
Siva Reddy
Website
Github
Google Scholar

Publications

BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 … (see more)presents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent’s architecture in three minor ways. This increases reinforcement learning sample efficiency by up to 3 × and improves imitation learning performance on the hardest level from 77% to 90 . 4% . We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.
2021-01-01
(published)
www.semanticscholar.org
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (see more)resents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent's architecture in three minor ways. This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90.4 %. We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.
2020-07-24
ArXiv (preprint)
arxiv.org
arxiv.org
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (see more)resents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent's architecture in three minor ways. This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90.4 %. We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.
2020-07-24
ArXiv (preprint)
arxiv.org
arxiv.org
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (see more)resents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent's architecture in three minor ways. This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90.4 %. We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.
2020-07-24
ArXiv (preprint)
arxiv.org
arxiv.org
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
2020-07-24
ArXiv (preprint)
arxiv.org
arxiv.org
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Konrad Żołna
Chitwan Saharia
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
2020-04-03
AAAI Conference on Artificial Intelligence (published)
doi.org
CLOSURE: Assessing Systematic Generalization of CLEVR Models
Dzmitry Bahdanau
Harm de Vries
Timothy O'Donnell
Shikhar Murty
Philippe Beaudoin
Yoshua Bengio
Aaron Courville
2019-12-12
ArXiv (preprint)
arxiv.org
arxiv.org
Automated curriculum generation for Policy Gradients from Demonstrations
Anirudh Srinivasan
Dzmitry Bahdanau
Maxime Chevalier-Boisvert
Yoshua Bengio
2019-12-01
ArXiv (preprint)
arxiv.org
arxiv.org
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Salem Lahlou
Lucas Willems
Chitwan Saharia
Thien Huu Nguyen
Yoshua Bengio
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific … (see more)reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.
2019-01-01
ICLR.cc/2019/Conference (poster)
openreview.net
openreview.net
Systematic Generalization: What Is Required and Can It Be Learned?
Dzmitry Bahdanau*
Shikhar Murty*
Michael Noukhovitch
Thien Huu Nguyen
Harm de Vries
Aaron Courville
2019-01-01
ICLR.cc/2019/Conference (poster)
openreview.net
openreview.net
BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Salem Lahlou
Lucas Willems
Chitwan Saharia
Thien Huu Nguyen
Yoshua Bengio
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific … (see more)reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.
2018-10-18
arXiv.org (preprint)
dblp.uni-trier.de