16 citations · 21 across the 4 of their papers we have counts for
4 papers
Analytical Probability Distributions and EM-Learning for Deep Generative Networks
Randall Balestriero, Sebastien Paris, Richard G. Baraniuk
Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs). In the absence of a known an…
Max-Affine Spline Insights into Deep Generative Networks
Randall Balestriero, Sebastien Paris, Richard Baraniuk
We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the lat…
Physeter catodon localization by sparse coding
Sébastien Paris, Yann Doh, Hervé Glotin +2
This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in comp…
Sparse Penalty in Deep Belief Networks: Using the Mixed Norm Constraint
Xanadu Halkias, Sebastien Paris, Herve Glotin
Deep Belief Networks (DBN) have been successfully applied on popular machine learning tasks. Specifically, when applied on hand-written digit recognition, DBNs have achieved approx…