Jordan Boyd-Graber

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Philip ResnikProfessor, Linguistics and UMIACS, University of MarylandZweryfikowany adres z umd.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandZweryfikowany adres z umiacs.umd.edu
Mohit IyyerAssociate Professor, University of Massachusetts AmherstZweryfikowany adres z cs.umass.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityZweryfikowany adres z columbia.edu
Yuening HuComputer Science, University of Maryland, College ParkZweryfikowany adres z cs.umd.edu
Alison M Smith-RennerDataminrZweryfikowany adres z cs.umd.edu
Shi FengUniversity of ChicagoZweryfikowany adres z uchicago.edu
Leah FindlaterUniversity of Washington & AppleZweryfikowany adres z uw.edu
Jonathan ChangFacebookZweryfikowany adres z fb.com
Viet-An NguyenResearch Scientist, MetaZweryfikowany adres z fb.com
Ke ZhaiUniversity of Maryland, College ParkZweryfikowany adres z umd.edu
Kevin SeppiProfessor of Computer Science, Brigham Young UniversityZweryfikowany adres z cs.byu.edu
Pedro RodriguezResearch Scientist, FacebookZweryfikowany adres z fb.com
Varun ManjunathaResearch Scientist, Adobe ResearchZweryfikowany adres z cs.umd.edu
He HeNew York UniversityZweryfikowany adres z cs.nyu.edu
Alvin Grissom IIAssistant Professor of Computer Science, Haverford CollegeZweryfikowany adres z haverford.edu
Eric WallaceUC BerkeleyZweryfikowany adres z berkeley.edu
Chen ZhaoNew York UniversityZweryfikowany adres z nyu.edu
Leonardo ClaudinoHCPS-NINDS-NIHZweryfikowany adres z nih.gov
Chong WangAppleZweryfikowany adres z cs.princeton.edu

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Jordan Boyd-Graber

Associate Professor, University of Maryland

Zweryfikowany adres z umiacs.umd.edu - Strona główna

Machine Learning Computational Linguistics Natural Language Processing Topic Models Question Answering


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Reading tea leaves: How humans interpret topic models J Chang, J Boyd-Graber, S Gerrish, C Wang, DM Blei Neural Information Processing Systems (NIPS) 31, 2009	3076	2009
Deep unordered composition rivals syntactic methods for text classification M Iyyer, V Manjunatha, J Boyd-Graber, H Daumé III Proceedings of the 53rd annual meeting of the association for computational …, 2015	1035	2015
Interactive topic modeling Y Hu, J Boyd-Graber, B Satinoff, A Smith Machine learning 95, 423-469, 2014	457	2014
A neural network for factoid question answering over paragraphs M Iyyer, J Boyd-Graber, L Claudino, R Socher, H Daumé III Proceedings of the 2014 conference on empirical methods in natural language …, 2014	446	2014
Political ideology detection using recursive neural networks M Iyyer, P Enns, J Boyd-Graber, P Resnik Proceedings of the 52nd annual meeting of the Association for Computational …, 2014	363	2014
A topic model for word sense disambiguation J Boyd-Graber, D Blei, X Zhu Conference on Empirical Methods in Natural Language Processing (EMNLP), 1024 …, 2007	333	2007
Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016	316	2016
Applications of topic models J Boyd-Graber, Y Hu, D Mimno Foundations and Trends® in Information Retrieval 11 (2-3), 143-296, 2017	314	2017
Pathologies of neural models make interpretations difficult S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber arXiv preprint arXiv:1804.07781, 2018	298	2018
Syntactic topic models J Boyd-Graber, DM Blei Neural Information Processing Systems (NIPS), 2008	283	2008
Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter P Resnik, W Armstrong, L Claudino, T Nguyen, VA Nguyen, ... Proceedings of the 2nd workshop on computational linguistics and clinical …, 2015	244	2015
Care and feeding of topic models: Problems, diagnostics, and improvements J Boyd-Graber, D Mimno, D Newman Handbook of mixed membership models and their applications 225255, 2014	235	2014
Multilingual topic models for unaligned text J Boyd-Graber, D Blei arXiv preprint arXiv:1205.2657, 2012	219	2012
Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce K Zhai, J Boyd-Graber, N Asadi, ML Alkhouja Proceedings of the 21st international conference on World Wide Web, 879-888, 2012	204	2012
Adding dense, weighted connections to WordNet J Boyd-Graber, C Fellbaum, D Osherson, R Schapire Proceedings of the third international WordNet conference, 29-36, 2006	189	2006
Connections between the lines: augmenting social networks with text J Chang, J Boyd-Graber, DM Blei Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	175	2009
Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships M Iyyer, A Guha, S Chaturvedi, J Boyd-Graber, H Daumé III	171	2016
Can you unpack that? learning to rewrite questions-in-context A Elgohary, D Peskov, J Boyd-Graber Can You Unpack That? Learning to Rewrite Questions-in-Context, 2019	170	2019
Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation J Boyd-Graber, P Resnik Proceedings of the 2010 Conference on Empirical Methods in Natural Language …, 2010	159	2010
Participatory design with proxies: developing a desktop-PDA system to support people with aphasia JL Boyd-Graber, SS Nikolova, KA Moffatt, KC Kin, JY Lee, LW Mackey, ... Proceedings of the SIGCHI conference on Human Factors in computing systems …, 2006	139	2006

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