Search Results for author: Tristan Deleu

Found 18 papers, 9 papers with code

Continuous-Time Meta-Learning with Forward Mode Differentiation

no code implementations ICLR 2022 Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field.

Few-Shot Image Classification Meta-Learning

Bayesian Structure Learning with Generative Flow Networks

no code implementations28 Feb 2022 Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, Yoshua Bengio

In Bayesian structure learning, we are interested in inferring a distribution over the directed acyclic graph (DAG) structure of Bayesian networks, from data.

Variational Inference

Rethinking Learning Dynamics in RL using Adversarial Networks

1 code implementation27 Jan 2022 Ramnath Kumar, Tristan Deleu, Yoshua Bengio

We present a learning mechanism for reinforcement learning of closely related skills parameterized via a skill embedding space.

reinforcement-learning

The Effect of Diversity in Meta-Learning

1 code implementation27 Jan 2022 Ramnath Kumar, Tristan Deleu, Yoshua Bengio

In this work, we find evidence to the contrary; we study different task distributions on a myriad of models and datasets to evaluate the effect of task diversity on meta-learning algorithms.

Few-Shot Learning

GFlowNet Foundations

no code implementations17 Nov 2021 Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, Emmanuel Bengio

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function.

Active Learning

Structured Sparsity Inducing Adaptive Optimizers for Deep Learning

1 code implementation7 Feb 2021 Tristan Deleu, Yoshua Bengio

The parameters of a neural network are naturally organized in groups, some of which might not contribute to its overall performance.

Natural Language Processing

Predicting Infectiousness for Proactive Contact Tracing

1 code implementation ICLR 2021 Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

Curriculum in Gradient-Based Meta-Reinforcement Learning

no code implementations19 Feb 2020 Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Chris J. Pal, Liam Paull

However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions.

Meta-Learning Meta Reinforcement Learning +2

The TCGA Meta-Dataset Clinical Benchmark

1 code implementation18 Oct 2019 Mandana Samiei, Tobias Würfl, Tristan Deleu, Martin Weiss, Francis Dutil, Thomas Fevens, Geneviève Boucher, Sebastien Lemieux, Joseph Paul Cohen

Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals.

Decision Making

Torchmeta: A Meta-Learning library for PyTorch

5 code implementations14 Sep 2019 Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, Yoshua Bengio

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research.

Meta-Learning

Learning Powerful Policies by Using Consistent Dynamics Model

1 code implementation11 Jun 2019 Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang

There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment.

Atari Games Model-based Reinforcement Learning

Gradient-Based Neural DAG Learning

1 code implementation ICLR 2020 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.

Causal Inference

The effects of negative adaptation in Model-Agnostic Meta-Learning

no code implementations5 Dec 2018 Tristan Deleu, Yoshua Bengio

The capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key factor in the recent success of these methods on few-shot learning problems.

Few-Shot Learning Meta Reinforcement Learning +1

Learning powerful policies and better dynamics models by encouraging consistency

no code implementations27 Sep 2018 Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Jian Tang

Analogously, we would expect such interaction to be helpful for a learning agent while learning to model the environment dynamics.

Model-based Reinforcement Learning

Learning Operations on a Stack with Neural Turing Machines

no code implementations2 Dec 2016 Tristan Deleu, Joseph Dureau

Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed recently to address the difficulty of storing information over long time periods.

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