Publications

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

A Singular Value Thresholding Algorithm for Matrix Completion

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understoo...

Fast Training of Support Vector Machines Using Sequential Minimal Optimization

This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the soluti...

Deep Sparse Rectifier Neural Networks

While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows...

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse ...

Image Super-Resolution Via Sparse Representation

This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-...

Optimal approximations by piecewise smooth functions and associated variational problems

Exact Matrix Completion via Convex Optimization

Atomic Decomposition by Basis Pursuit

The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries---stationary wavelets, wavelet packets, cosine packets...

Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38

State of the art simulations of aortic haemodynamics feature full fluid-structure interaction (FSI) and coupled 0D boundary conditions. Such analyses require not only significan...

An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

Abstract We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the ...

MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package

Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...