4 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning Normalized Energy Models for Linear Inverse Problems
Nicolas Zilberstein, Santiago Segarra, Eero Simoncelli +1
Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from two key limitations: the p…
Learning normalized image densities via dual score matching
Florentin Guth, Zahra Kadkhodaie, Eero P Simoncelli
Learning probability models from data is at the heart of many machine learning endeavors, but is notoriously difficult due to the curse of dimensionality. We introduce a new framew…
On the universality of neural encodings in CNNs
Florentin Guth, Brice Ménard
We explore the universality of neural encodings in convolutional neural networks trained on image classification tasks. We develop a procedure to directly compare the learned weigh…
Separation and Concentration in Deep Networks
John Zarka, Florentin Guth, Stéphane Mallat
Numerical experiments demonstrate that deep neural network classifiers progressively separate class distributions around their mean, achieving linear separability on the training s…