Jeffrey A. Bilmes

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William Stafford NobleProfessor of Genome Sciences, University of WashingtonVerified email at uw.edu
Rishabh IyerUniversity of Texas Dallas, Microsoft, UWVerified email at utdallas.edu
Katrin KirchhoffAmazonVerified email at amazon.com
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
Kai WeiGraduate student of Electrical Engineering, University of WashingtonVerified email at uw.edu
Michael M. HoffmanChair and Senior Scientist, University Health Network/Associate Professor, University of TorontoVerified email at utoronto.ca
Stefanie JegelkaTUM and MITVerified email at mit.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
Yuzong LiuAmazonVerified email at amazon.com
Krste AsanovicUC BerkeleyVerified email at berkeley.edu
Tanzeem ChoudhuryProfessor, Computing and Information Science, Cornell TechVerified email at cornell.edu
Danny WyattMetaVerified email at meta.com
Dieter FoxUniversity of Washington and NVIDIAVerified email at cs.washington.edu
Nelson MorganICSIVerified email at icsi.berkeley.edu
Richard VuducGeorgia Institute of TechnologyVerified email at cc.gatech.edu
Franz PernkopfGraz University of TechnologyVerified email at tugraz.at
Daniel P W EllisResearch Scientist, GoogleVerified email at google.com

Jeffrey A. Bilmes

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

Verified email at uw.edu - Homepage

machine learning and artificial intelligence combinatorial optimization speech and langauge


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An integrated encyclopedia of DNA elements in the human genome ENCODE Project Consortium Nature 489 (7414), 57, 2012	17477	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	4011	1998
Deep canonical correlation analysis G Andrew, R Arora, J Bilmes, K Livescu International conference on machine learning, 1247-1255, 2013	2435	2013
On deep multi-view representation learning W Wang, R Arora, K Livescu, J Bilmes International conference on machine learning, 1083-1092, 2015	1199	2015
A class of submodular functions for document summarization H Lin, J Bilmes Proceedings of the 49th annual meeting of the association for computational …, 2011	973	2011
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	796	1997
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	747	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	624	2013
On mixup training: Improved calibration and predictive uncertainty for deep neural networks S Thulasidasan, G Chennupati, JA Bilmes, T Bhattacharya, S Michalak Advances in neural information processing systems 32, 2019	614	2019
Multi-document summarization via budgeted maximization of submodular functions H Lin, J Bilmes Human Language Technologies: The 2010 Annual Conference of the North …, 2010	530	2010
Submodularity in data subset selection and active learning K Wei, R Iyer, J Bilmes International conference on machine learning, 1954-1963, 2015	488	2015
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	396	2003
Coresets for data-efficient training of machine learning models B Mirzasoleiman, J Bilmes, J Leskovec International Conference on Machine Learning, 6950-6960, 2020	394	2020
What HMMs can do JA Bilmes IEICE TRANSACTIONS on Information and Systems 89 (3), 869-891, 2006	348	2006
Submodular optimization with submodular cover and submodular knapsack constraints RK Iyer, JA Bilmes Advances in neural information processing systems 26, 2013	322	2013
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	304	2008
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	273	2002
MVA processing of speech features CP Chen, JA Bilmes IEEE Transactions on Audio, Speech, and Language Processing 15 (1), 257-270, 2006	271	2006
Submodularity beyond submodular energies: coupling edges in graph cuts S Jegelka, J Bilmes CVPR 2011, 1897-1904, 2011	246	2011
Learning mixtures of submodular functions for image collection summarization S Tschiatschek, RK Iyer, H Wei, JA Bilmes Advances in neural information processing systems 27, 2014	243	2014

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