Search Results for author: Charles Ollion

Found 13 papers, 8 papers with code

Learning Natural Language Generation with Truncated Reinforcement Learning

1 code implementation NAACL 2022 Alice Martin, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin

To our knowledge, it is the first approach that successfully learns a language generation policy without pre-training, using only reinforcement learning.

Language Modelling Question Generation +4

Diffusion bridges vector quantized Variational AutoEncoders

1 code implementation10 Feb 2022 Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines

In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously.

NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform

1 code implementation NeurIPS 2021 Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian Robert

Sampling from a complex distribution $\pi$ and approximating its intractable normalizing constant $\mathrm{Z}$ are challenging problems.

Learning Natural Language Generation from Scratch

no code implementations20 Sep 2021 Alice Martin Donati, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin

This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement learning (RL).

Language Modelling reinforcement-learning +2

NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform

1 code implementation17 Mar 2021 Achille Thin, Yazid Janati, Sylvain Le Corff, Charles Ollion, Arnaud Doucet, Alain Durmus, Eric Moulines, Christian Robert

Sampling from a complex distribution $\pi$ and approximating its intractable normalizing constant Z are challenging problems.

Joint self-supervised blind denoising and noise estimation

1 code implementation16 Feb 2021 Jean Ollion, Charles Ollion, Elisabeth Gassiat, Luc Lehéricy, Sylvain Le Corff

Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data.

Image Denoising Noise Estimation

CoRe: Color Regression for Multicolor Fashion Garments

no code implementations6 Oct 2020 Alexandre Rame, Arthur Douillard, Charles Ollion

That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture.

regression

The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction

no code implementations15 Jul 2020 Alice Martin, Charles Ollion, Florian Strub, Sylvain Le Corff, Olivier Pietquin

This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture.

Insights from the Future for Continual Learning

1 code implementation24 Jun 2020 Arthur Douillard, Eduardo Valle, Charles Ollion, Thomas Robert, Matthieu Cord

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting.

Class Incremental Learning Representation Learning +1

PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning

2 code implementations ECCV 2020 Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, Eduardo Valle

Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning.

Class Incremental Learning Incremental Learning +1

DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine

1 code implementation17 Mar 2020 Jean Ollion, Charles Ollion

The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy.

Multi-Object Tracking regression +1

OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation

no code implementations6 Dec 2018 Alexandre Rame, Emilien Garreau, Hedi Ben-Younes, Charles Ollion

Similarly to self-training methods, the predictions of these initial detectors mitigate the missing annotations on the complementary datasets.

Domain Adaptation object-detection +2

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