Jordan Boyd-Graber

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Philip ResnikProfessor, Linguistics and UMIACS, University of MarylandDirección de correo verificada de umd.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandDirección de correo verificada de umiacs.umd.edu
Mohit IyyerAssociate Professor, University of MarylandDirección de correo verificada de umd.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityDirección de correo verificada de columbia.edu
Yuening HuComputer Science, University of Maryland, College ParkDirección de correo verificada de cs.umd.edu
Shi FengThe George Washington UniversityDirección de correo verificada de gwu.edu
Alison M Smith-RennerDataminrDirección de correo verificada de cs.umd.edu
Leah FindlaterUniversity of Washington & AppleDirección de correo verificada de uw.edu
Jonathan ChangFacebookDirección de correo verificada de fb.com
Viet-An NguyenResearch Scientist, MetaDirección de correo verificada de meta.com
Kevin SeppiProfessor of Computer Science, Brigham Young UniversityDirección de correo verificada de cs.byu.edu
Pedro RodriguezResearch Scientist, FacebookDirección de correo verificada de fb.com
Ke ZhaiUniversity of Maryland, College ParkDirección de correo verificada de umd.edu
Chen ZhaoNYU Shanghai / New York UniversityDirección de correo verificada de nyu.edu
Alvin Grissom IIAssociate Professor, Haverford CollegeDirección de correo verificada de haverford.edu
Chenglei SiStanford UniversityDirección de correo verificada de stanford.edu
He HeNew York UniversityDirección de correo verificada de cs.nyu.edu
Varun ManjunathaSenior Research Scientist, Adobe ResearchDirección de correo verificada de adobe.com
Eric WallaceUC BerkeleyDirección de correo verificada de berkeley.edu
Chong WangAppleDirección de correo verificada de cs.princeton.edu

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Jordan Boyd-Graber

Full Professor, University of Maryland

Dirección de correo verificada de umiacs.umd.edu - Página principal

Machine Learning Computational Linguistics Natural Language Processing Topic Models Question Answering


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	3506	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	1153	2015
Interactive topic modeling Y Hu, J Boyd-Graber, B Satinoff, A Smith Machine learning 95, 423-469, 2014	509	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	469	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	406	2014
Applications of topic models J Boyd-Graber, Y Hu, D Mimno Foundations and Trends® in Information Retrieval 11 (2-3), 143-296, 2017	404	2017
Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016	394	2016
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	379	2018
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	338	2007
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	298	2015
Syntactic topic models J Boyd-Graber, DM Blei Neural Information Processing Systems (NIPS), 2008	287	2008
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	286	2014
Prompting gpt-3 to be reliable C Si, Z Gan, Z Yang, S Wang, J Wang, J Boyd-Graber, L Wang arXiv preprint arXiv:2210.09150, 2022	256	2022
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	241	2019
Multilingual topic models for unaligned text J Boyd-Graber, D Blei arXiv preprint arXiv:1205.2657, 2012	223	2012
Cold-start active learning through self-supervised language modeling M Yuan, HT Lin, J Boyd-Graber arXiv preprint arXiv:2010.09535, 2020	210	2020
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	200	2006
Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships M Iyyer, A Guha, S Chaturvedi, J Boyd-Graber, H Daumé III	183	2016
Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber Transactions of the Association for Computational Linguistics 7, 387-401, 2019	180	2019

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