Tim Vieira

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Ryan CotterellETH Zürich, University of CambridgeVerified email at inf.ethz.ch
Jason EisnerProfessor of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
Clara MeisterETH ZürichVerified email at inf.ethz.ch
Dan RothProfessor of Computer Science, University of PennsylvaniaVerified email at seas.upenn.edu
Benjamin Van DurmeJohns Hopkins University; Microsoft Semantic MachinesVerified email at cs.jhu.edu
Keisuke SakaguchiAllen Institute for Artificial Intelligence (AI2)Verified email at allenai.org
Sheng ZhangMicrosoft ResearchVerified email at microsoft.com
Kyle RawlinsAssociate Professor, Cognitive Science, Johns Hopkins UniversityVerified email at jhu.edu
Rachel RudingerAssistant Professor, Department of Computer Science, University of MarylandVerified email at umd.edu
Aaron Steven WhiteAssistant Professor, Department of Linguistics, University of RochesterVerified email at rochester.edu
Ran ZmigrodComputer Science PhD Student, University of CambridgeVerified email at cam.ac.uk
Dan GoldwasserPurdue UniversityVerified email at purdue.edu
Nikhil JohriSoftware Engineering Manager at FacebookVerified email at stanford.edu
Ming-Wei ChangGoogle ResearchVerified email at google.com
Gourab KunduUniversity of Illinois at Urbana ChampaignVerified email at illinois.edu
Quang DoUniversity of Illinois, FacebookVerified email at fb.com
Vivek SrikumarAssociate Professor, School of Computing, University of UtahVerified email at cs.utah.edu
Kevin SmallMachine Learning Scientist, AmazonVerified email at amazon.com
V.G.Vinod VydiswaranAssistant Professor of Learning Health Sciences and of Information, University of MichiganVerified email at umich.edu
Joshua S. RuleUniversity of California, BerkeleyVerified email at berkeley.edu

Tim Vieira

Johns Hopkins University

Verified email at cs.jhu.edu - Homepage

Machine Learning Natural Language Processing Reinforcement Learning Declarative Methods


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Universal Decompositional Semantics on Universal Dependencies AS White, D Reisinger, K Sakaguchi, T Vieira, S Zhang, R Rudinger, ... Empirical Methods in Natural Language Processing, 2016	109	2016
Reasoning about quantities in natural language S Roy, T Vieira, D Roth Transactions of the Association for Computational Linguistics 3, 1-13, 2015	91	2015
Relation Alignment for Textual Entailment Recognition. M Sammons, VGV Vydiswaran, T Vieira, N Johri, MW Chang, ... TAC, 2009	52	2009
A Joint Model of Orthography and Morphological Segmentation R Cotterell, T Vieira, H Schütze NAACL, 2016	38	2016
If beam search is the answer, what was the question? C Meister, T Vieira, R Cotterell arXiv preprint arXiv:2010.02650, 2020	36	2020
Best-first beam search C Meister, T Vieira, R Cotterell Transactions of the Association for Computational Linguistics 8, 795-809, 2020	16	2020
Learning to prune: Exploring the frontier of fast and accurate parsing T Vieira, J Eisner Transactions of the Association for Computational Linguistics 5, 263-278, 2017	10	2017
The universal decompositional semantics dataset and decomp toolkit AS White, E Stengel-Eskin, S Vashishtha, V Govindarajan, DA Reisinger, ... arXiv preprint arXiv:1909.13851, 2019	9	2019
Speed-accuracy tradeoffs in tagging with variable-order CRFs and structured sparsity T Vieira, R Cotterell, J Eisner Proceedings of the 2016 Conference on Empirical Methods in Natural Language …, 2016	9	2016
Grammarless parsing for joint inference J Naradowsky, T Vieira, DA Smith Proceedings of COLING 2012, 1995-2010, 2012	9	2012
Gumbel-max trick and weighted reservoir sampling T Vieira	7	2014
Gumbel-max trick and weighted reservoir sampling, 2014 T Vieira URL https://timvieira. github. io/blog/post/2014/08/01/gumbel-max-trick …, 0	7
Dyna: Toward a self-optimizing declarative language for machine learning applications T Vieira, M Francis-Landau, NW Filardo, F Khorasani, J Eisner Proceedings of the 1st ACM SIGPLAN International Workshop on Machine …, 2017	6	2017
Estimating means in a finite universe, 2017 T Vieira URL https://timvieira. github. io/blog/post/2017/07/03/estimating-means-in-a …, 2017	5	2017
Please mind the root: Decoding arborescences for dependency parsing R Zmigrod, T Vieira, R Cotterell arXiv preprint arXiv:2010.02550, 2020	4	2020
Efficient computation of expectations under spanning tree distributions R Zmigrod, T Vieira, R Cotterell Transactions of the Association for Computational Linguistics 9, 675-690, 2021	2	2021
Exp-normalize trick T Vieira	2
Conditional Poisson Stochastic Beams CI Meister, A Amini, T Vieira, R Cotterell Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021	1	2021
Searching for More Efficient Dynamic Programs T Vieira, R Cotterell, J Eisner arXiv preprint arXiv:2109.06966, 2021		2021
Efficient Sampling of Dependency Structures R Zmigrod, T Vieira, R Cotterell arXiv preprint arXiv:2109.06521, 2021		2021

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