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no code implementations • 2 Nov 2020 • Thibault Lesieur, Jérémie Messud, Issa Hammoud, Hanyuan Peng, Céline Lacombe, Paulien Jeunesse

To train a deep neural network to mimic the outcomes of processing sequences, a version of Conditional Generalized Adversarial Network (CGAN) can be used.

no code implementations • NeurIPS Workshop ICBINB 2020 • Thibault Lesieur, Jérémie Messud, Issa Hammoud, Hanyuan Peng, Céline Lacombe, Paulien Jeunesse

To train a deep neural network to mimic the outcomes of processing sequences, a version of Conditional Generalized Adversarial Network (CGAN) can be used.

no code implementations • 10 Oct 2016 • Thibault Lesieur, Caterina De Bacco, Jess Banks, Florent Krzakala, Cris Moore, Lenka Zdeborová

We consider the problem of Gaussian mixture clustering in the high-dimensional limit where the data consists of $m$ points in $n$ dimensions, $n, m \rightarrow \infty$ and $\alpha = m/n$ stays finite.

no code implementations • NeurIPS 2016 • Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborova

We also show that for a large set of parameters, an iterative algorithm called approximate message-passing is Bayes optimal.

1 code implementation • 14 Jul 2015 • Thibault Lesieur, Florent Krzakala, Lenka Zdeborová

This paper considers probabilistic estimation of a low-rank matrix from non-linear element-wise measurements of its elements.

1 code implementation • 1 Mar 2015 • Thibault Lesieur, Florent Krzakala, Lenka Zdeborova

We study optimal estimation for sparse principal component analysis when the number of non-zero elements is small but on the same order as the dimension of the data.

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