13 papers
GEOPHYS: The Geometry of Physical Plausibility
Christian Internò, Alexander Pondaven, Habon Issa +8
While humans can identify physically implausible events within milliseconds, machine learning approaches addressing the same problem are extremely slow and expensive. They either r…
Learning a Maximum Entropy Model for Visual Textures using Diffusion
Xinyuan Zhao, Eero P. Simoncelli
Visual textures -- spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree) -- are ubiquitous in visual scenes and provide impor…
Blind denoising diffusion models and the blessings of dimensionality
Zahra Kadkhodaie, Aram-Alexandre Pooladian, Sinho Chewi +1
Denoising diffusion models (DDMs) are state-of-the-art methods for learning densities from data across numerous domains, yet many aspects of the training and sampling pipeline rema…
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 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…