Jeffrey A. Bilmes - Publications

Affiliations: 
Computer Science and Engineering University of Washington, Seattle, Seattle, WA 
Area:
Computer Science, Artificial Intelligence

44 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any innacuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2016 Halloran JT, Bilmes JA, Noble WS. A dynamic Bayesian network for accurate detection of peptides from tandem mass spectra. Journal of Proteome Research. PMID 27397138 DOI: 10.1021/acs.jproteome.6b00290  1
2016 Wang S, Halloran JT, Bilmes JA, Noble WS. Faster and more accurate graphical model identification of tandem mass spectra using trellises. Bioinformatics (Oxford, England). 32: i322-i331. PMID 27307634 DOI: 10.1093/bioinformatics/btw269  1
2016 Jegelka S, Bilmes JA. Graph cuts with interacting edge weights: examples, approximations, and algorithms Mathematical Programming. 1-42. DOI: 10.1007/s10107-016-1038-y  1
2015 Libbrecht MW, Ay F, Hoffman MM, Gilbert DM, Bilmes JA, Noble WS. Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression. Genome Research. 25: 544-57. PMID 25677182 DOI: 10.1101/gr.184341.114  1
2015 Wang W, Arora R, Livescu K, Bilmes JA. Unsupervised learning of acoustic features via deep canonical correlation analysis Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 2015: 4590-4594. DOI: 10.1109/ICASSP.2015.7178840  1
2015 Kawahara Y, Iyer R, Bilmes JA. On approximate non-submodular minimization via tree-structured supermodularity Journal of Machine Learning Research. 38: 444-452.  1
2015 Libbrecht MW, Hoffman MM, Bilmes JA, Noble WS. Entropic graph-based posterior regularization 32nd International Conference On Machine Learning, Icml 2015. 3: 1992-2001.  1
2014 Halloran JT, Bilmes JA, Noble WS. Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference On Uncertainty in Artificial Intelligence. 30: 320-329. PMID 25298752  1
2013 Hoffman MM, Ernst J, Wilder SP, Kundaje A, Harris RS, Libbrecht M, Giardine B, Ellenbogen PM, Bilmes JA, Birney E, Hardison RC, Dunham I, Kellis M, Noble WS. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Research. 41: 827-41. PMID 23221638 DOI: 10.1093/nar/gks1284  1
2013 Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. Unsupervised pattern discovery in human chromatin structure through genomic segmentation 2013 Acm Conference On Bioinformatics, Computational Biology and Biomedical Informatics, Acm-Bcb 2013. 813-814. DOI: 10.1145/2506583.2506701  1
2012 Singh AP, Halloran J, Bilmes JA, Kirchoff K, Noble WS. Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra. Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference On Uncertainty in Artificial Intelligence. 28: 775-785. PMID 25383048  1
2012 Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nature Methods. 9: 473-6. PMID 22426492 DOI: 10.1038/nmeth.1937  1
2011 Bartels CD, Bilmes JA. Creating non-minimal triangulations for use in inference in mixed stochastic/deterministic graphical models Machine Learning. 84: 249-289. DOI: 10.1007/s10994-010-5233-4  1
2010 Reynolds SM, Bilmes JA, Noble WS. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens. Plos Computational Biology. 6: e1000834. PMID 20628623 DOI: 10.1371/journal.pcbi.1000834  1
2010 Chen X, Hoffman MM, Bilmes JA, Hesselberth JR, Noble WS. A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data. Bioinformatics (Oxford, England). 26: i334-42. PMID 20529925 DOI: 10.1093/bioinformatics/btq175  1
2010 Bartels CD, Bilmes JA. Graphical models for integrating syllabic information Computer Speech and Language. 24: 685-697. DOI: 10.1016/j.csl.2009.11.001  1
2010 Reynolds SM, Weng Z, Bilmes JA, Noble WS. Predicting nucleosome positioning using multiple evidence tracks Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6044: 441-455. DOI: 10.1007/978-3-642-12683-3_29  1
2010 Pernkopf F, Bilmes JA. Efficient heuristics for discriminative structure learning of Bayesian network classifiers Journal of Machine Learning Research. 11: 2323-2360.  1
2009 Harada S, Wobbrock JO, Malkin J, Bilmes JA, Landay JA. Longitudinal study of people learning to use continuous voice-based cursor control Conference On Human Factors in Computing Systems - Proceedings. 347-356. DOI: 10.1145/1518701.1518757  1
2009 Ma N, Bartels CD, Bilmes JA, Green PD. Modelling the prepausal lengthening effect for speech recognition: A dynamic Bayesian network approach Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 4617-4620. DOI: 10.1109/ICASSP.2009.4960659  1
2009 Pernkopf F, Van Pham T, Bilmes JA. Broad phonetic classification using discriminative Bayesian networks Speech Communication. 51: 151-166. DOI: 10.1016/j.specom.2008.07.003  1
2009 Reynolds SM, Bilmes JA, Noble WS. On the relationship between DNA periodicity and local chromatin structure Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5541: 434-450. DOI: 10.1007/978-3-642-02008-7_31  1
2009 Kawahara Y, Nagano K, Tsuda K, Bilmes JA. Submodularity cuts and applications Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 916-924.  1
2008 Reynolds SM, Käll L, Riffle ME, Bilmes JA, Noble WS. Transmembrane topology and signal peptide prediction using dynamic bayesian networks. Plos Computational Biology. 4: e1000213. PMID 18989393 DOI: 10.1371/journal.pcbi.1000213  1
2008 Klammer AA, Reynolds SM, Bilmes JA, MacCoss MJ, Noble WS. Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification. Bioinformatics (Oxford, England). 24: i348-56. PMID 18586734 DOI: 10.1093/bioinformatics/btn189  1
2008 Harada S, Landay JA, Malkin J, Li X, Bilmes JA. The Vocal Joystick: evaluation of voice-based cursor control techniques for assistive technology. Disability and Rehabilitation. Assistive Technology. 3: 22-34. PMID 18416516 DOI: 10.1080/17483100701352963  1
2008 Bartels CD, Bilmes JA. Using syllable nuclei locations to improve automatic speech recognition in the presence of burst noise Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 2406-2409.  1
2007 Chen CP, Bilmes JA. MVA processing of speech features Ieee Transactions On Audio, Speech and Language Processing. 15: 257-270. DOI: 10.1109/TASL.2006.876717  1
2007 Bilmes JA. Submodularity and adaptation 2007 Ieee Workshop On Automatic Speech Recognition and Understanding, Asru 2007, Proceedings. 249.  1
2007 Filali K, Bilmes JA. Multi-dynamic Bayesian networks Advances in Neural Information Processing Systems. 409-416.  1
2007 Bartels CD, Bilmes JA. Use of syllable nuclei locations to improve ASR 2007 Ieee Workshop On Automatic Speech Recognition and Understanding, Asru 2007, Proceedings. 335-340.  1
2006 Li X, Malkin J, Bilmes JA. A high-speed, low-resource ASR back-end based on custom arithmetic Ieee Transactions On Audio, Speech and Language Processing. 14: 1683-1693. DOI: 10.1109/TSA.2005.858556  1
2006 Bilmes JA. What HMMs can do Ieice Transactions On Information and Systems. 869-891. DOI: 10.1093/ietisy/e89-d.3.869  1
2006 Bartels CD, Bilmes JA. Non-minimal triangulations for mixed stochastic/deterministic graphical models Proceedings of the 22nd Conference On Uncertainty in Artificial Intelligence, Uai 2006. 15-22.  1
2006 Kilanski K, Malkin J, Xiao L, Wright R, Bilmes JA. The vocal joystick data collection effort and vowel corpus Interspeech 2006 and 9th International Conference On Spoken Language Processing, Interspeech 2006 - Icslp. 3: 1073-1076.  1
2005 Bilmes JA, Bartels C. Graphical model architectures for speech recognition Ieee Signal Processing Magazine. 22: 89-100. DOI: 10.1109/MSP.2005.1511827  1
2005 Li Y, Shapiro LG, Bilmes JA. A generative/discriminative learning algorithm for image classification Proceedings of the Ieee International Conference On Computer Vision. 1605-1612. DOI: 10.1109/ICCV.2005.7  1
2005 Reynolds SM, Bilmes JA. Part-of-speech tagging using virtual evidence and negative training Hlt/Emnlp 2005 - Human Language Technology Conference and Conference On Empirical Methods in Natural Language Processing, Proceedings of the Conference. 459-466.  1
2005 Bilmes JA, Li X, Malkin J, Kilanski K, Wright R, Kirchhoff K, Subramanya A, Harada S, Landay JA, Dowden P, Chizeck H. The vocal joystick: A voice-based human-computer interface for individuals with motor impairments Hlt/Emnlp 2005 - Human Language Technology Conference and Conference On Empirical Methods in Natural Language Processing, Proceedings of the Conference. 995-1002.  1
2004 Li Y, Bilmes JA, Shapiro LG. Object class recognition using images of abstract regions Proceedings - International Conference On Pattern Recognition. 1: 40-43. DOI: 10.1109/ICPR.2004.1334000  1
2004 Vuduc R, Demmel JW, Bilmes JA. Statistical models for empirical search-based performance tuning International Journal of High Performance Computing Applications. 18: 65-94.  1
2003 Russell MJ, Bilmes JA. Introduction to the special issue on new computational paradigms for acoustic modeling in speech recognition Computer Speech and Language. 17: 107-112. DOI: 10.1016/S0885-2308(03)00013-5  1
2003 Bilmes JA. Buried Markov models: A graphical-modeling approach to automatic speech recognition Computer Speech and Language. 17: 213-231. DOI: 10.1016/S0885-2308(03)00010-X  1
2003 Bilmes JA, Kirchhoff K. Generalized rules for combination and joint training of classifiers Pattern Analysis and Applications. 6: 201-211. DOI: 10.1007/s10044-002-0188-0  1
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