U NIVERSITY OF T ORONTO , D EPARTMENT OF C OMPUTER S CIENCE 6 King'sCollegeRd. LP283 Toronto, M5S 3G4 CANADA • T EL : 416.978.7391 F AX : 416.978.1455 • www.cs.toronto.edu/˜roweis/ firstname.lastname@example.org S AM T. R OWEIS C URRICULUM V ITAE Date of Birth: April 27,1972.
Roweis Gatsby Computational Neuroscience Unit, UCL 17 QueenSquare, London WC1N 3AR, UK email@example.com Abstract Many problems in information processing involve some form of dimension-alityreduction.
REVIEW Communicated by Steven Nowlan A Unifying Review of Linear Gaussian Models Sam Roweis ⁄ Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, U.S.A. Zoubin Ghahramani ⁄ Department of Computer Science, University of Toronto, Toronto, Canada Factor ...
SamT.Roweis ROWEIS@CS.TORONTO.EDU Departmentof ComputerScience University ofToronto 6King’sCollegeRoad PrattBuilding283 Toronto,OntarioM5S 3G4,CANADA
Factorial Models and Refiltering for Speech Separation and Denoising SamT. Roweis Department of Computer Science, University of Toronto, firstname.lastname@example.org Abstract This paper proposes the combination of several ideas, some old and some new, from machine learning and speech processing.
Global Coordinationof Local Linear Models Sam Roweis y , LawrenceK. Saul yy , and GeoffreyE. Hinton y y Departmentof ComputerScience, Universityof Toronto
Saul&S. T. Roweis, Think globally, fit locally: unsupervised learning of low dimensional manifolds, Journal of Machine Learning Research, v. 4, pp. 119-155,2003.
In Section 2, we outline SNE as presented by Hintonand Roweis (2002), which forms the basis fort-SNE. In Section 3, we presentt-SNE, which has two important differences from SNE.
... Shoukry Roweis, offered the following 13 points (a to m) as a list of our desired outcomes for CUSP, noting that achieving one or more would constitute success. ...
Another way to understand this problem is the probabilistic interpretation of PCA[Tipping and Bishop, 1999; Roweis, 1997], where principal components corresponds to the maximum likelihood estimation (MLE ...