Jordan Boyd-Graber

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Philip ResnikProfessor, Linguistics and UMIACS, University of MarylandPatvirtintas el. paštas umd.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandPatvirtintas el. paštas umiacs.umd.edu
Mohit IyyerAssociate Professor, University of MarylandPatvirtintas el. paštas cs.umass.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityPatvirtintas el. paštas columbia.edu
Yuening HuComputer Science, University of Maryland, College ParkPatvirtintas el. paštas cs.umd.edu
Shi FengThe George Washington UniversityPatvirtintas el. paštas gwu.edu
Alison M Smith-RennerDataminrPatvirtintas el. paštas cs.umd.edu
Leah FindlaterUniversity of Washington & ApplePatvirtintas el. paštas uw.edu
Jonathan ChangFacebookPatvirtintas el. paštas fb.com
Viet-An NguyenResearch Scientist, MetaPatvirtintas el. paštas meta.com
Kevin SeppiProfessor of Computer Science, Brigham Young UniversityPatvirtintas el. paštas cs.byu.edu
Pedro RodriguezResearch Scientist, FacebookPatvirtintas el. paštas fb.com
Ke ZhaiUniversity of Maryland, College ParkPatvirtintas el. paštas umd.edu
Chen ZhaoNYU Shanghai / New York UniversityPatvirtintas el. paštas nyu.edu
Alvin Grissom IIAssociate Professor, Haverford CollegePatvirtintas el. paštas haverford.edu
Chenglei SiStanford UniversityPatvirtintas el. paštas stanford.edu
He HeNew York UniversityPatvirtintas el. paštas cs.nyu.edu
Varun ManjunathaSenior Research Scientist, Adobe ResearchPatvirtintas el. paštas adobe.com
Eric WallaceUC BerkeleyPatvirtintas el. paštas berkeley.edu
Chong WangApplePatvirtintas el. paštas cs.princeton.edu

Stebėti

Jordan Boyd-Graber

Full Professor, University of Maryland

Patvirtintas el. paštas umiacs.umd.edu - Pagrindinis puslapis

Machine Learning Computational Linguistics Natural Language Processing Topic Models Question Answering


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	3491	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	1149	2015
Interactive topic modeling Y Hu, J Boyd-Graber, B Satinoff, A Smith Machine learning 95, 423-469, 2014	505	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	468	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	405	2014
Applications of topic models J Boyd-Graber, Y Hu, D Mimno Foundations and Trends® in Information Retrieval 11 (2-3), 143-296, 2017	402	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	393	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	373	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	296	2015
Syntactic topic models J Boyd-Graber, DM Blei Neural Information Processing Systems (NIPS), 2008	286	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	285	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	250	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	240	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	206	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	199	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	179	2019

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