PDF-MaximumMargin Matrix Factorization Nathan Srebro Dept

Author : natalia-silvester | Published Date : 2014-12-18

of Computer Science University of Toronto Toronto ON CANADA naticstorontoedu Jason D M Rennie Tommi S Jaakkola Computer Science and Arti64257cial Intelligence Lab

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "MaximumMargin Matrix Factorization Natha..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

MaximumMargin Matrix Factorization Nathan Srebro Dept: Transcript

of Computer Science University of Toronto Toronto ON CANADA naticstorontoedu Jason D M Rennie Tommi S Jaakkola Computer Science and Arti64257cial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA jrennietommicsailmitedu Abstrac. umdedu Dept of Computer Science University of Maryland College Park M D 20742 USA Vikas Sindhwani vsindhwusibmcom IBM TJ Watson Research Center Yorktown Heights NY 10598 US Prabhanjan Kambadur pkambaduusibmcom IBM TJ Watson Research CenterYorktown He . Factorization. Yingzhou. . Li,. . Haizhao. . Yang,. . Eileen. . Martin,. . Kenneth. . Ho,. . Lexing. . Ying. Complementary. . low-rank. . property. Non-uniform Fourier Transform. Hankel. 1. Recovering latent factors in a matrix. m. columns. v11. …. …. …. vij. …. vnm. n . rows. 2. Recovering latent factors in a matrix. K * m. n * K. x1. y1. x2. y2. ... ... …. …. xn. yn. a1. 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. Prime and Composite Numbers. Prime Number. A prime number is any whole number that has only two factors, itself and 1. . Example:. 5. It only has two factors, 5 and 1. 5 x 1= 5. What are other examples of prime numbers?. 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. by Carol Edelstein. Definition. Product. – An answer to a multiplication problem.. . 7 x 8 = 56. Product. Definition. Factor. – a number that is multiplied by another to give a product.. . 7 x 8 = 56. 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. ... ... …. 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 . 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. ORTHOGONALIZATION AND. LEAST SQUARES. -Mohammed. BEST GROUP. CONTENTS. Householder and Givens Transformations. The QR Factorization. The Full-Rank Least Squares Problem. Other Orthogonal Factorizations. Everyday Math Lesson 1.9. Lesson Objectives. I can tell the difference between powers of ten written as ten raised to an exponent. .. I can show powers of 10 using whole number exponents. . Mental Math. 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 . KeywordsFactorization G-ECM CADO-NFS NFS RSA ECMINTRODUCTIONPublic key cryptography based on complexity of hard problem in mathematics Security in some current cryptography methods like RSA public key

Download Document

Here is the link to download the presentation.
"MaximumMargin Matrix Factorization Nathan Srebro Dept"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.