Marylou Gabrié | DeepAI

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∙ 06/01/2023

Balanced Training of Energy-Based Models with Adaptive Flow Sampling

Energy-based models (EBMs) are versatile density estimation models that ...

0 Louis Grenioux, et al. ∙

∙ 02/09/2023

On Sampling with Approximate Transport Maps

Transport maps can ease the sampling of distributions with non-trivial g...

0 Louis Grenioux, et al. ∙

∙ 10/25/2021

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals

Markov Chain Monte Carlo (MCMC) methods are a powerful tool for computat...

0 James A. Brofos, et al. ∙

∙ 07/16/2021

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods

Normalizing flows can generate complex target distributions and thus sho...

10 Marylou Gabrié, et al. ∙

∙ 07/11/2021

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks

Energy-based models (EBMs) are generative models that are usually traine...

6 Carles Domingo Enrich, et al. ∙

∙ 03/09/2021

More data or more parameters? Investigating the effect of data structure on generalization

One of the central features of deep learning is the generalization abili...

0 Stéphane d'Ascoli, et al. ∙

∙ 12/14/2020

Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging

Phase retrieval is the inverse problem of recovering a signal from magni...

13 Hannah Lawrence, et al. ∙

∙ 11/03/2019

Mean-field inference methods for neural networks

Machine learning algorithms relying on deep neural networks recently all...

12 Marylou Gabrié, et al. ∙

∙ 10/01/2019

Blind calibration for compressed sensing: State evolution and an online algorithm

Compressed sensing, allows to acquire compressible signals with a small ...

0 Marylou Gabrié, et al. ∙

∙ 05/24/2018

Entropy and mutual information in models of deep neural networks

We examine a class of deep learning models with a tractable method to co...

4 Marylou Gabrié, et al. ∙

∙ 02/10/2017

A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines

Restricted Boltzmann machines (RBMs) are energy-based neural-networks wh...

0 Eric W. Tramel, et al. ∙

∙ 06/13/2016

Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines

In this work, we consider compressed sensing reconstruction from M measu...

0 Eric W. Tramel, et al. ∙

∙ 06/09/2015

Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy

Restricted Boltzmann machines are undirected neural networks which have ...

0 Marylou Gabrié, et al. ∙

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