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Found 5 papers, 0 papers with code
Learning sparse structures for physics-inspired compressed sensing
Estimating these wavenumbers is of key interest to understand the propagating environment as well as the emitting source.
Random Projections through multiple optical scattering: Approximating kernels at the speed of light
Random projections have proven extremely useful in many signal processing and machine learning applications.
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that is the recovery of a sequence of high-resolution images from its low-resolution counterpart.
Approximate Message Passing with Restricted Boltzmann Machine Priors
Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem.
Statistical inference with probabilistic graphical models
These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013.
