Search Results for author: Corentin Tallec

Found 15 papers, 8 papers with code

Large-Scale Representation Learning on Graphs via Bootstrapping

3 code implementations12 Feb 2021 Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Veličković, Michal Valko

To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.

Contrastive Learning Graph Representation Learning +1

Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint

no code implementations18 Jan 2021 Léonard Blier, Corentin Tallec, Yann Ollivier

In reinforcement learning, temporal difference-based algorithms can be sample-inefficient: for instance, with sparse rewards, no learning occurs until a reward is observed.

Making Deep Q-learning methods robust to time discretization

1 code implementation28 Jan 2019 Corentin Tallec, Léonard Blier, Yann Ollivier

Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018).


Mixed batches and symmetric discriminators for GAN training

no code implementations ICML 2018 Thomas Lucas, Corentin Tallec, Jakob Verbeek, Yann Ollivier

We propose to feed the discriminator with mixed batches of true and fake samples, and train it to predict the ratio of true samples in the batch.

Can recurrent neural networks warp time?

1 code implementation ICLR 2018 Corentin Tallec, Yann Ollivier

Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms.

Unbiasing Truncated Backpropagation Through Time

no code implementations ICLR 2018 Corentin Tallec, Yann Ollivier

Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT) while relieving the need for a complete backtrack through the whole data sequence at every step.

Language Modelling

Unbiased Online Recurrent Optimization

1 code implementation ICLR 2018 Corentin Tallec, Yann Ollivier

The novel Unbiased Online Recurrent Optimization (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models.

Training recurrent networks online without backtracking

no code implementations28 Jul 2015 Yann Ollivier, Corentin Tallec, Guillaume Charpiat

The evolution of this search direction is partly stochastic and is constructed in such a way to provide, at every time, an unbiased random estimate of the gradient of the loss function with respect to the parameters.

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