Lecture 4: Modeling Sparse Matrix-Vector Multiply William Gropp www.cs.illinois.edu/~wgropp Sustained Memory Bandwidth

Source URL: wgropp.cs.illinois.edu

• Central processing unit
• Computer memory
• Cache
• Programming languages
• CPU cache
• Sparse matrix
• Processor register
• Fortran
• Data

MONAURAL SEPARATION AND CLASSIFICATION OF NON-LINEAR TRANSFORMED INDEPENDENT SIGNALS: AN SVM PERSPECTIVE Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309

Source URL: www.bioinf.jku.at

• Signal processing
• Statistical models
• Convex optimization
• Operations research
• Independent component analysis
• Wavelet
• Linear programming
• Support vector machine
• Matrix
• Machine learning
• Sparse approximation
• Sparse dictionary learning

1030 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 3, MARCH 2006 Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise

Source URL: users.cms.caltech.edu

• Linear algebra
• Convex analysis
• Mathematical optimization
• Numerical linear algebra
• Signal processing
• Sparse approximation
• Convex function
• Norm
• Vector space
• Banach space
• Preconditioner
• Convex set

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 1 Codebook based Audio Feature Representation for Music Information Retrieval

Source URL: bmcfee.github.io

• Vectors
• Abstract algebra
• Linear algebra
• Vector quantization
• Representation theory of Lie groups
• Lie algebras
• Code
• K-means clustering
• Lasso
• Four-vector
• Euclidean vector
• Sparse approximation

Sparse Matrix-Vector Multiply on the Keystone II Digital Signal Processor

Source URL: www.ieee-hpec.org

Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning Yuhui Quan1 , Yan Huang2 , Hui Ji1 1 2

Source URL: www.cv-foundation.org

• Algebra
• Linear algebra
• Mathematics
• Machine learning
• Numerical linear algebra
• Signal processing
• Sparse dictionary learning
• Unsupervised learning
• Sparse approximation
• Feature learning
• K-SVD
• Support vector machine

On Boosting Sparse Parities Lev Reyzin Department of Mathematics, Statistics, & Computer Science University of Illinois at Chicago Chicago, IL 60607

Source URL: www.levreyzin.com

• Ensemble learning
• AdaBoost
• Boosting
• BrownBoost
• LPBoost
• Decision stump
• LogitBoost
• Support vector machine
• Machine learning
• Supervised learning
• Parity
• Online machine learning

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: REGULAR PAPERS, VOL. 59, NO. 1, JANUARYAn I/O Bandwidth-Sensitive Sparse Matrix-Vector Multiplication Engine on FPGAs

Source URL: www.ece.iastate.edu

SOS Lecture 4: Finding planted sparse vector and dictionary learning / Boaz Barak Based on scribed (and greatly expanded) notes by Samuel Hopkins and Jerry Li Finding Sparse Planted Vector 1

Source URL: www.boazbarak.org

Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore Platforms Samuel Williams∗†, Leonid Oliker∗, Richard Vuduc§, John Shalf∗, Katherine Yelick∗†, James Demmel† CRD/NERSC, Lawrence Ber

Source URL: cscads.rice.edu