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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 ...
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02/09/2023
On Sampling with Approximate Transport Maps
Transport maps can ease the sampling of distributions with non-trivial g...
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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...
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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...
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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...
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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...
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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...
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11/03/2019
Mean-field inference methods for neural networks
Machine learning algorithms relying on deep neural networks recently all...
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10/01/2019
Blind calibration for compressed sensing: State evolution and an online algorithm
Compressed sensing, allows to acquire compressible signals with a small ...
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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...
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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...
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06/13/2016
Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines
In this work, we consider compressed sensing reconstruction from M measu...
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06/09/2015