Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Ollion, Charles"'
Vector Quantized-Variational AutoEncoders (VQ-VAE) are generative models based on discrete latent representations of the data, where inputs are mapped to a finite set of learned embeddings.To generate new samples, an autoregressive prior distribution
Externí odkaz:
http://arxiv.org/abs/2202.04895
Autor:
Donati, Alice Martin, Quispe, Guillaume, Ollion, Charles, Corff, Sylvain Le, Strub, Florian, Pietquin, Olivier
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). AsRL methods unsuccessfully scale to large action spaces,
Externí odkaz:
http://arxiv.org/abs/2109.09371
Autor:
Thin, Achille, Janati, Yazid, Corff, Sylvain Le, Ollion, Charles, Doucet, Arnaud, Durmus, Alain, Moulines, Eric, Robert, Christian
Sampling from a complex distribution $\pi$ and approximating its intractable normalizing constant Z are challenging problems. In this paper, a novel family of importance samplers (IS) and Markov chain Monte Carlo (MCMC) samplers is derived. Given an
Externí odkaz:
http://arxiv.org/abs/2103.10943
We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the noisy observations are independent conditionally to the signal, the ne
Externí odkaz:
http://arxiv.org/abs/2102.08023
Publikováno v:
CVPR 2022, Workshop on Computer Vision for Fashion, Art, and Design
Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyon
Externí odkaz:
http://arxiv.org/abs/2010.02849
This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture. The keys, queries, values and attention vectors of the network are considered as t
Externí odkaz:
http://arxiv.org/abs/2007.08620
Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this work, we propose prescient continual learning, a novel experimental s
Externí odkaz:
http://arxiv.org/abs/2006.13748
Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning. In this work, we propose PODNet, a model inspired by representati
Externí odkaz:
http://arxiv.org/abs/2004.13513
Autor:
Ollion, Jean, Ollion, Charles
The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy. It has become a valuable tool for single-cell level quantitative analysis and characterization of many cellu
Externí odkaz:
http://arxiv.org/abs/2003.07790
Object detectors tend to perform poorly in new or open domains, and require exhaustive yet costly annotations from fully labeled datasets. We aim at benefiting from several datasets with different categories but without additional labelling, not only
Externí odkaz:
http://arxiv.org/abs/1812.02611