Low Power Sparse Approximation on Reconfigurable Analog Hardware Samuel Shapero Student Member, IEEE*, Adam Charles Student Member, IEEE, Christopher Rozell Senior Member, IEEE, and Paul Hasler Senior Member, IEEE Abstr

Source URL: siplab.gatech.edu

Sparse BRDF Approximation using Compressive Sensing Benoît Zupancic, Cyril Soler To cite this version: Benoît Zupancic, Cyril Soler. Sparse BRDF Approximation using Compressive Sensing. 6th Siggraph Conference and Exhi

Source URL: hal.inria.fr

Approximation Bounds for Sparse Principal Component Analysis Alexandre d’Aspremont, CNRS & Ecole Polytechnique. With Francis Bach, INRIA-ENS and Laurent El Ghaoui, U.C. Berkeley.

Source URL: www.di.ens.fr

Approximation Bounds for Sparse PCA Alexandre d’Aspremont, CNRS & Ecole Polytechnique with Francis Bach, INRIA-ENS and Laurent El Ghaoui, U.C. Berkeley A. d’Aspremont

Source URL: www.di.ens.fr

Linear Algebraic Structure of Word Senses, with Applications to Polysemy arXiv:1601.03764v1 [cs.CL] 14 JanSanjeev Arora

Source URL: arxiv.org

• Linguistics
• Mathematics
• Lexical semantics
• Semantics
• Artificial neural networks
• Natural language processing
• Word-sense disambiguation
• Word embedding
• Word2vec
• Sparse approximation
• Word-sense induction
• Discourse analysis

Norms of random submatrices and sparse approximation Joel A. Tropp 1 Applied & Computational Mathematics, California Institute of Technology, Pasadena, CAReceived *****; accepted after revision +++++ Present

Source URL: users.cms.caltech.edu

• Linear algebra
• Mathematics
• Matrix
• Norm

Multi-Stage Dantzig Selector Ji Liu, Peter Wonka, Jieping Ye Arizona State University {ji.liu,peter.wonka,jieping.ye}@asu.edu

Source URL: peterwonka.net

• Statistics
• Mathematical analysis
• Probability
• Probability distributions
• Operations research
• Convex optimization
• Normal distribution
• Linear regression
• Linear programming
• Sparse approximation

ON THE CONDITIONING OF RANDOM SUBDICTIONARIES JOEL A. TROPP Abstract. An important problem in the theory of sparse approximation is to identify wellconditioned subsets of vectors from a general dictionary. In most cases,

Source URL: users.cms.caltech.edu

• Algebra
• Mathematics
• Mathematical analysis
• Operator theory
• Linear algebra
• Matrix
• Basis
• Inner product space
• Partial differential equations
• Differential forms on a Riemann surface
• NeumannPoincar operator

I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

Source URL: mmds-data.org

• Algebra
• Mathematics
• Multivariate statistics
• Numerical analysis
• Numerical linear algebra
• Iterative methods
• Dimension reduction
• Principal component analysis
• Singular value decomposition
• Stochastic optimization
• Nonlinear dimensionality reduction
• Sparse dictionary learning

Simple, Efficient, and Neural Algorithms for Sparse Coding Sanjeev Arora∗ Princeton University, Computer Science Department

Source URL: jmlr.org

• Mathematics
• Numerical analysis
• Algebra
• Linear algebra
• Convex optimization
• Operations research
• Computational statistics
• Mathematical optimization
• K-SVD
• Sparse approximation
• Deep learning
• Stochastic gradient descent