Portrait de Dzmitry Bahdanau

Dzmitry Bahdanau

Membre industriel principal
Chaire en IA Canada-CIFAR
Professeur adjoint, McGill University, École d'informatique
Chercheur scientifique IA, ServiceNow
Sujets de recherche
Apprentissage profond
Traitement du langage naturel

Biographie

Dzmitry Bahdanau est professeur adjoint à l’Université McGill et chercheur à ServiceNow Element AI. Précédemment, il a obtenu son doctorat à l'Université de Montréal / Mila – Institut québécois d’intelligence artificielle en travaillant avec Yoshua Bengio. Il s'intéresse aux questions fondamentales et appliquées concernant la compréhension du langage naturel. Ses principaux domaines de recherche comprennent l'analyse sémantique, les interfaces utilisateur du langage, la généralisation systématique et les systèmes hybrides neuronaux symboliques.

Étudiants actuels

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Doctorat - McGill
Co-superviseur⋅e :

Publications

Combating False Negatives in Adversarial Imitation Learning
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the de… (voir plus)sired behavior. However, as the trained policy learns to be more successful, the negative examples (the ones produced by the agent) become increasingly similar to expert ones. Despite the fact that the task is successfully accomplished in some of the agent's trajectories, the discriminator is trained to output low values for them. We hypothesize that this inconsistent training signal for the discriminator can impede its learning, and consequently leads to worse overall performance of the agent. We show experimental evidence for this hypothesis and that the ‘False Negatives’ (i.e. successful agent episodes) significantly hinder adversarial imitation learning, which is the first contribution of this paper. Then, we propose a method to alleviate the impact of false negatives and test it on the BabyAI environment. This method consistently improves sample efficiency over the baselines by at least an order of magnitude.
Understanding by Understanding Not: Modeling Negation in Language Models
Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language mode… (voir plus)ls often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 … (voir plus)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.
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (voir plus)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.
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (voir plus)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.
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (voir plus)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.
BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
CLOSURE: Assessing Systematic Generalization of CLEVR Models
Automated curriculum generation for Policy Gradients from Demonstrations
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific … (voir plus)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.