5 papers
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…
There Will Be a Scientific Theory of Deep Learning
Jamie Simon, Daniel Kunin, Alexander Atanasov +11
In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the train…
Learning a distance measure from the information-estimation geometry of data
Guy Ohayon, Pierre-Etienne H. Fiquet, Florentin Guth +2
We introduce the Information-Estimation Metric (IEM), a novel form of distance function derived from an underlying continuous probability density over a domain of signals. The IEM…
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…
A Rainbow in Deep Network Black Boxes
Florentin Guth, Brice Ménard, Gaspar Rochette +1
A central question in deep learning is to understand the functions learned by deep networks. What is their approximation class? Do the learned weights and representations depend on…