PPT-Factorbird : a Parameter Server Approach to Distributed Matrix Factorization
Author : celsa-spraggs | Published Date : 2018-02-24
Sebastian Schelter Venu Satuluri Reza Zadeh Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014 Latent Factor Models
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Factorbird : a Parameter Server Approach to Distributed Matrix Factorization: Transcript
Sebastian Schelter Venu Satuluri Reza Zadeh Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014 Latent Factor Models Given M sparse n x . Andersen Jun Woo Park Alexander J Smola Amr Ahmed Vanja Josifovski James Long Eugene J Shekita BorYiing Su Carnegie Mellon University Baidu Google muli dga junwoop cscmuedu alexsmolaorg amra vanjaj jamlong shekita boryiingsu googlecom Abstract Data Analysis on . MapReduce. Chao Liu, Hung-. chih. Yang, Jinliang Fan, Li-Wei He, Yi-Min Wang. Internet Services Research Center (ISRC). Microsoft Research Redmond. Internet Services Research Center (ISRC). . Networks. Distributed. . P. arameter. . Networks. Distributed. . Parameter. . Networks. 1.. The . electric. and . magnetic. . power. . distribute. . homogeneously. . along. . the. . wire. Recovering latent factors in a matrix. m. movies. v11. …. …. …. vij. …. vnm. V[. i,j. ] = user i’s rating of movie j. n . users. Recovering latent factors in a matrix. m. movies. n . users. T(A) . 1. 2. 3. 4. 6. 7. 8. 9. 5. 5. 9. 6. 7. 8. 1. 2. 3. 4. 1. 5. 2. 3. 4. 9. 6. 7. 8. A . 9. 1. 2. 3. 4. 6. 7. 8. 5. G(A) . Symmetric-pattern multifrontal factorization. T(A) . 1. 2. 3. 4. 6. 7. 8. under Additional Constraints. Kaushik . Mitra. . University . of Maryland, College Park, MD . 20742. Sameer . Sheorey. y. Toyota Technological Institute, . Chicago. Rama . Chellappa. University of Maryland, College Park, MD 20742. and. Collaborative Filtering. 1. Matt Gormley. Lecture . 26. November 30, 2016. School of Computer Science. Readings:. Koren. et al. (2009). Gemulla. et al. (2011). 10-601B Introduction to Machine Learning. By M. Li, D. Anderson, J. Park, A. . Smola. , A. Ahmed, V. . Josifovski. , J. Long E. . Shekita. , B. Su. . EECS 582 – W16. 1. Outline. Motivation. Parameter Server architecture. Why is it special?. Grayson Ishihara. Math 480. April 15, 2013. Topics at Hand. What is Partial Pivoting?. What is the PA=LU Factorization?. What kinds of things can we use these tools for?. Partial Pivoting. Used to solve matrix equations. m. movies. v11. …. …. …. vij. …. vnm. V[. i,j. ] = user i’s rating of movie j. n . users. Recovering latent factors in a matrix. m. movies. n . users. m. movies. x1. y1. x2. y2. ... ... …. Dileep Mardham. Introduction. Sparse Direct Solvers is a fundamental tool in scientific computing. Sparse factorization can be a challenge to accelerate using GPUs. GPUs(Graphics Processing Units) can be quite good for accelerating sparse direct solvers. Gemar. 11-10-12. Advisor: Dr. . Rebaza. Overview. Definitions. Theorems. Proofs. Examples. Physical Applications. Definition 1. We say that a subspace S or . R. n. is invariant under . A. nxn. , or A-invariant if:. Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Course Project. Write-ups due June 1. st. Project presentations . 12 presentations, 10 mins each, 15 min slack. Sebastian . Schelter. , . Venu. . Satuluri. , Reza . Zadeh. Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014. Latent Factor Models. Given . M. sparse. n . x .
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