1 citations · 1 across the 17 of their papers we have counts for
17 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…
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…
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…
Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
We present the Score-based Autoencoder for Multiscale Inference (SAMI), a method for unsupervised representation learning that combines the theoretical frameworks of diffusion mode…
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…
Unconditional CNN denoisers contain sparse semantic representation of images
Zahra Kadkhodaie, Stéphane Mallat, Eero Simoncelli
Generative diffusion models learn probability densities over diverse image datasets by estimating the score with a neural network trained to remove noise. Despite their remarkable…
Detection of Moving Objects Using Self-motion Constraints on Optic Flow
Hope Lutwak, Bas Rokers, Eero P. Simoncelli
As we move through the world, the pattern of light projected on our eyes is complex and dynamic, yet we are still able to distinguish between moving and stationary objects. We prop…
Learning predictable and robust neural representations by straightening image sequences
Xueyan Niu, Cristina Savin, Eero P. Simoncelli
Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate…
Video prediction using score-based conditional density estimation
Pierre-Étienne H. Fiquet, Eero P. Simoncelli
Temporal prediction is inherently uncertain, but representing the ambiguity in natural image sequences is a challenging high-dimensional probabilistic inference problem. For natura…
Feature-guided score diffusion for sampling conditional densities
Zahra Kadkhodaie, Stéphane Mallat, Eero P. Simoncelli
Score diffusion methods can learn probability densities from samples. The score of the noise-corrupted density is estimated using a deep neural network, which is then used to itera…
Discriminating image representations with principal distortions
Jenelle Feather, David Lipshutz, Sarah E. Harvey +2
Image representations (artificial or biological) are often compared in terms of their global geometric structure; however, representations with similar global structure can have st…
Shaping the distribution of neural responses with interneurons in a recurrent circuit model
David Lipshutz, Eero P. Simoncelli
Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. L…
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
Ling-Qi Zhang, Zahra Kadkhodaie, Eero P. Simoncelli +1
We examine the problem of selecting a small set of linear measurements for reconstructing high-dimensional signals. Well-established methods for optimizing such measurements includ…
Layerwise complexity-matched learning yields an improved model of cortical area V2
Nikhil Parthasarathy, Olivier J. Hénaff, Eero P. Simoncelli
Human ability to recognize complex visual patterns arises through transformations performed by successive areas in the ventral visual cortex. Deep neural networks trained end-to-en…
Generalization in diffusion models arises from geometry-adaptive harmonic representations
Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli +1
Deep neural networks (DNNs) trained for image denoising are able to generate high-quality samples with score-based reverse diffusion algorithms. These impressive capabilities seem…