Jeffrey A. Bilmes

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William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Katrin KirchhoffAmazonVerified email at amazon.com
Rishabh IyerUniversity of Texas Dallas, Microsoft, UWVerified email at utdallas.edu
Hui LinChief Scientist of LAIX; former Research Scientist at GoogleVerified email at google.com
Amarnag SubramanyaSenior Research Scientist, Google ResearchVerified email at google.com
Karen LivescuTTI-ChicagoVerified email at ttic.edu
Michael M. HoffmanSenior Scientist, University Health Network/Associate Professor, University of TorontoVerified email at utoronto.ca
Stefanie JegelkaMITVerified email at mit.edu
Kai WeiGraduate student of Electrical Engineering, University of WashingtonVerified email at uw.edu
Maxwell W LibbrechtAssistant Professor, Computing Science, Simon Fraser UniversityVerified email at sfu.ca
James LandayProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Krste AsanovicUC BerkeleyVerified email at berkeley.edu
Tanzeem ChoudhuryProfessor, Computing and Information Science, Cornell TechVerified email at cornell.edu
Danny WyattGoogleVerified email at google.com
Yuzong LiuAmazonVerified email at amazon.com
Dieter FoxUniversity of Washington and NVIDIAVerified email at cs.washington.edu
Franz PernkopfGraz University of TechnologyVerified email at tugraz.at
Nelson MorganICSIVerified email at icsi.berkeley.edu
Kevin DuhJohns Hopkins UniversityVerified email at cs.jhu.edu
Richard VuducGeorgia Institute of TechnologyVerified email at cc.gatech.edu

Jeffrey A. Bilmes

Professor of Electrical Engineering and Computer Science, University of Washington Seattle

Verified email at uw.edu - Homepage

machine learning and artificial intelligence combinatorial optimization speech and langauge processing bioinformatics high p


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An integrated encyclopedia of DNA elements in the human genome ENCODE Project Consortium Nature 489 (7414), 57, 2012	12512	2012
A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models JA Bilmes International Computer Science Institute 4 (510), 126, 1998	3559	1998
Deep canonical correlation analysis G Andrew, R Arora, J Bilmes, K Livescu International conference on machine learning, 1247-1255, 2013	1357	2013
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology J Bilmes, K Asanovic, CW Chin, J Demmel ACM International Conference on Supercomputing 25th Anniversary Volume, 253-260, 1997	763	1997
A class of submodular functions for document summarization H Lin, J Bilmes Proceedings of the 49th annual meeting of the association for computational …, 2011	737	2011
On deep multi-view representation learning W Wang, R Arora, K Livescu, J Bilmes International conference on machine learning, 1083-1092, 2015	618	2015
Unsupervised pattern discovery in human chromatin structure through genomic segmentation MM Hoffman, OJ Buske, J Wang, Z Weng, JA Bilmes, WS Noble Nature methods 9 (5), 473-476, 2012	604	2012
Integrative annotation of chromatin elements from ENCODE data MM Hoffman, J Ernst, SP Wilder, A Kundaje, RS Harris, M Libbrecht, ... Nucleic acids research 41 (2), 827-841, 2013	525	2013
Multi-document summarization via budgeted maximization of submodular functions H Lin, J Bilmes Human Language Technologies: The 2010 Annual Conference of the North …, 2010	420	2010
Factored language models and generalized parallel backoff J Bilmes, K Kirchhoff Companion Volume of the Proceedings of HLT-NAACL 2003-Short Papers, 4-6, 2003	386	2003
What HMMs can do JA Bilmes IEICE TRANSACTIONS on Information and Systems 89 (3), 869-891, 2006	303	2006
The graphical models toolkit: An open source software system for speech and time-series processing J Bilmes, G Zweig 2002 IEEE International Conference on Acoustics, Speech, and Signal …, 2002	275	2002
MVA processing of speech features CP Chen, JA Bilmes IEEE Transactions on Audio, Speech, and Language Processing 15 (1), 257-270, 2006	266	2006
Transmembrane topology and signal peptide prediction using dynamic bayesian networks SM Reynolds, L Käll, ME Riffle, JA Bilmes, WS Noble PLoS computational biology 4 (11), e1000213, 2008	242	2008
Submodularity in data subset selection and active learning K Wei, R Iyer, J Bilmes International Conference on Machine Learning, 1954-1963, 2015	225	2015
Submodular optimization with submodular cover and submodular knapsack constraints R Iyer, J Bilmes arXiv preprint arXiv:1311.2106, 2013	211	2013
Submodularity beyond submodular energies: coupling edges in graph cuts S Jegelka, J Bilmes CVPR 2011, 1897-1904, 2011	209	2011
Dynamic bayesian multinets JA Bilmes arXiv preprint arXiv:1301.3837, 2013	193	2013
Timing is of the Essence: Perceptual and Computational Techniques for Representing, Learning, and Reproducing Expressive Timing in Percussive Rhythm JA Bilmes MASSACHUSETTS INSTITUTE OF TECHNOLOGY, 1993	189	1993
Learning mixtures of submodular shells with application to document summarization H Lin, JA Bilmes arXiv preprint arXiv:1210.4871, 2012	186	2012

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