'Lda' presentation slideshows

Topic Models for Morphologically Rich Languages

Topic Models for Morphologically Rich Languages. Michael Elhadad, Meni Adler, Yoav Goldberg, Rafi Cohen 23 Jan 2011, Haifa Machine Translation and Morphologically-rich Languages. Topic Models. Unsupervised discovery of topics in text collection Useful to browse/explore large corpora by theme

848 views • 64 slides

Gabriel Kotliar Physics Department and Center for Materials Theory Rutgers University

Gabriel Kotliar Physics Department and Center for Materials Theory Rutgers University. The Strong Correlation Problem. Two limiting cases of the electronic structure of solids are understood:the high density limit and the limit of well separated atoms.

657 views • 56 slides

Power Linear Discriminant Analysis (PLDA)

607 views • 38 slides

Linear Discriminant Analysis (LDA) for selection cuts :

539 views • 38 slides

Modeling Political Blog Posts with Response

Modeling Political Blog Posts with Response. Tae Yano Carnegie Mellon University taey@cs.cmu.edu IBM SMiLe Open House Yorktown Heights, NY October 8, 2009. Talk is about. How we are designing topic models for online political discussion. Political blogs.

342 views • 34 slides

Learning Difficulties Australia Presentation to CCD Stakeholders Workshop

Learning Difficulties Australia Presentation to CCD Stakeholders Workshop Macquarie University 29 April 2014. Our Mission

403 views • 32 slides

A Latent Dirichlet Allocation Method For Selectional Preferences

314 views • 31 slides

Semi-supervised Discriminant Analysis

393 views • 25 slides

Joint Modeling of Entity-Entity Links and Entity-Annotated Text

246 views • 24 slides

243 views • 23 slides

Hydrogen Absorbing Materials

Hydrogen Absorbing Materials. YOSHIDA Lab. M1 Ryusuke Tominaga. The purpose of research.

371 views • 21 slides

Improved Speaker Adaptation Using Speaker Dependent Feature Projections

Improved Speaker Adaptation Using Speaker Dependent Feature Projections. Spyros Matsoukas and Richard Schwartz Sep. 5, 2003 Martigny, Switzerland. Overview. Baseline system Technical background Heteroscedastic Linear Discriminant Analysis (HLDA)

311 views • 16 slides

Recognition with Expression Variations

237 views • 16 slides

Computational Design of Strongly Correlated Materials

Computational Design of Strongly Correlated Materials. Sergej Savrasov. Gabriel Kotliar. Supported by NSF ITR 0342290 (NJIT), 0312478 (Rutgers) NSF CAREER 02382188 (NJIT) NSF DMR 0096462 (Rutgers). Content. New Spectral Density Functional Theory to Computations of Materials

327 views • 14 slides

Computational Design of Strongly Correlated Materials

Computational Design of Strongly Correlated Materials. Sergej Savrasov. Gabriel Kotliar. Supported by NSF ITR 0342290 (NJIT), 0312478 (Rutgers) NSF CAREER 02382188 (NJIT) NSF DMR 0096462 (Rutgers). Content. New Spectral Density Functional Theory to Computations of Materials

287 views • 14 slides

Named Entity Mining From Click-Through Data Using Weakly Supervised LDA

Named Entity Mining From Click-Through Data Using Weakly Supervised LDA. Gu Xu 1 , Shuang -Hong Yang 1,2 , Hang Li 1 1 Microsoft Research Asia, China 2 College of Computing, Georgia Tech, USA. Talk Outline. Named Entity Mining Exploiting click-through data

236 views • 14 slides

Geci Karuri-Sebina, PhD Candidate, gecik@yahoo SARCHi Research Seminar

249 views • 13 slides

Strasbourg 05/06/07

247 views • 9 slides

Band structure of strongly correlated materials from the Dynamical Mean Field perspective

K Haule Rutgers University Collaborators : J.H. Shim & Gabriel Kotliar. Band structure of strongly correlated materials from the Dynamical Mean Field perspective. Fe, Ni. As, P. La,Sm,Ce. O. Iron based high-Tc superconductors. x~5-20%. Smaller c Higher T c.

189 views • 7 slides

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30 views • 2 slides