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06/06/2022
Why do CNNs Learn Consistent Representations in their First Layer Independent of Labels and Architecture?
It has previously been observed that the filters learned in the first la...
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03/22/2022
Generating natural images with direct Patch Distributions Matching
Many traditional computer vision algorithms generate realistic images by...
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04/20/2021
Posterior Sampling for Image Restoration using Explicit Patch Priors
Almost all existing methods for image restoration are based on optimizin...
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07/24/2020
The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation is an inherently ill-posed probl...
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02/20/2020
A Bayes-Optimal View on Adversarial Examples
The ability to fool modern CNN classifiers with tiny perturbations of th...
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05/31/2018
On GANs and GMMs
A longstanding problem in machine learning is to find unsupervised metho...
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05/30/2018
Why do deep convolutional networks generalize so poorly to small image transformations?
Deep convolutional network architectures are often assumed to guarantee ...
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08/18/2016
A Tight Convex Upper Bound on the Likelihood of a Finite Mixture
The likelihood function of a finite mixture model is a non-convex functi...
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04/11/2016
Statistics of RGBD Images
Cameras that can measure the depth of each pixel in addition to its colo...
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04/11/2016
Beyond Brightness Constancy: Learning Noise Models for Optical Flow
Optical flow is typically estimated by minimizing a "data cost" and an o...
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09/26/2013
Tighter Linear Program Relaxations for High Order Graphical Models
Graphical models with High Order Potentials (HOPs) have received conside...
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01/23/2013
Loopy Belief Propagation for Approximate Inference: An Empirical Study
Recently, researchers have demonstrated that loopy belief propagation - ...
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01/10/2013
The Factored Frontier Algorithm for Approximate Inference in DBNs
The Factored Frontier (FF) algorithm is a simple approximate inferenceal...
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06/20/2012
MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies
Finding the most probable assignment (MAP) in a general graphical model ...
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06/13/2012
Tightening LP Relaxations for MAP using Message Passing
Linear Programming (LP) relaxations have become powerful tools for findi...
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05/09/2012