1 citations · 1 across the 17 of their papers we have counts for
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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…