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Relevance Vector Machines with Empirical Likelihood-Ratio Kernels for PLDA Speaker Verification

Relevance Vector Machines with Empirical Likelihood-Ratio Kernels for PLDA Speaker Verification

Relevance Vector Machines with Empirical Likelihood-Ratio Kernels for PLDA Speaker Verification. Wei Rao and Man-Wai Mak. ISCSLP 2014. Department of Electronic and Information Engineering The Hong Kong Polytechnic University, Hong Kong SAR, China. Summary.

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554 views • 26 slides
G22.3250-001

G22.3250-001

G22.3250-001. Recoverable Virtual Memory. Robert Grimm New York University. Altogether Now: The Three Questions. What is the problem? What is new or different? What are the contributions and limitations?. The Goal. Simplify the implementation of fault-tolerant distributed applications

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285 views • 19 slides
RED 600

RED 600

RED 600. BLACK WHITE GUM 979. SHECKLER 5 BLACK GREY ORANGE - 577. SHECKLER 6 BLACK BLUE - 587. @ DEREKGOMESSURF. BARGE LS NAGY TAN - 468. BARGE LS RED WHITE GREY - 623. BARGE LS BLACK BLK WHITE - 623. BARGE LS BLACK GREY WHITE - 581 DESPACHO ENERO 2013.

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251 views • 12 slides
Dept of Computer & Information Sciences University of Delaware

Dept of Computer & Information Sciences University of Delaware

Dept of Computer & Information Sciences University of Delaware. JikesRVM. Contents. Introduction to JikesRVM Optimizations IRs (Intermediate Representations) Editing JikesRVM Steps For Project1 Questions. Introduction to JikesRVM. This is not a Java source compiler.

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121 views • 12 slides
Multivariate Relevance Vector Machines For Tracking

Multivariate Relevance Vector Machines For Tracking

Multivariate Relevance Vector Machines For Tracking. UNIVERSITY OF CAMBRIDGE. Graz, Austria. A. Thayananthan, R. Navaratnam, B. Stenger, P. H. S. Torr and R. Cipolla. Learning a Sparse One-to-Many Mapping. Contributions. An extension of the RVM algorithm to multivariate outputs (MVRVM)

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86 views • 2 slides
Multivariate Relevance Vector Machines For Tracking

Multivariate Relevance Vector Machines For Tracking

Multivariate Relevance Vector Machines For Tracking. UNIVERSITY OF CAMBRIDGE. Graz, Austria. A. Thayananthan, R. Navaratnam, B. Stenger, P. H. S. Torr and R. Cipolla. Learning a Sparse One-to-Many Mapping. Contributions. An extension of the RVM algorithm to multivariate outputs (MVRVM)

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96 views • 2 slides

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