Sameer Singh

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Carlos GuestrinProfessor, Stanford UniversityDirección de correo verificada de stanford.edu
Marco Tulio RibeiroGoogle DeepMindDirección de correo verificada de cs.washington.edu
Matt GardnerScaled CognitionDirección de correo verificada de scaledcognition.com
Eric WallaceUC BerkeleyDirección de correo verificada de berkeley.edu
Robert L Logan IVUC IrvineDirección de correo verificada de uci.edu
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindDirección de correo verificada de cs.ucl.ac.uk
Dheeru DuaUniversity of California, IrvineDirección de correo verificada de uci.edu
Yasaman RazeghiUniversity of California, IrvineDirección de correo verificada de uci.edu
Dylan SlackGoogleDirección de correo verificada de google.com
Himabindu LakkarajuAssistant Professor, Harvard UniversityDirección de correo verificada de seas.harvard.edu
Tim RocktäschelDirector and Open-Endedness Team Lead at Google DeepMind, Professor of AI at UCL, Fellow ELLISDirección de correo verificada de cs.ucl.ac.uk
Shi FengThe George Washington UniversityDirección de correo verificada de gwu.edu
Tony Z. ZhaoStanford UniversityDirección de correo verificada de stanford.edu
Pouya PezeshkpourUniversity of California, IrvineDirección de correo verificada de uci.edu
Nitish GuptaSenior Research Scientist, Google DeepMindDirección de correo verificada de google.com
Michael WickUniversity of MassachusettsDirección de correo verificada de cs.umass.edu
Noah A. SmithUniversity of Washington; Allen Institute for Artificial IntelligenceDirección de correo verificada de cs.washington.edu
Yoshitomo MatsubaraSpiffy AIDirección de correo verificada de uci.edu
Sanjay SubramanianUC BerkeleyDirección de correo verificada de berkeley.edu

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Sameer Singh

Professor, UC Irvine | CTO, Spiffy AI

Dirección de correo verificada de uci.edu - Página principal

AI Machine Learning Natural Language Processing LLMs


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
"Why Should I Trust You?": Explaining the Predictions of Any Classifier MT Ribeiro, S Singh, C Guestrin Knowledge Discovery and Data Mining (KDD), 2016	22049	2016
Anchors: High-precision model-agnostic explanations MT Ribeiro, S Singh, C Guestrin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	2675	2018
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts T Shin, Y Razeghi, RL Logan IV, E Wallace, S Singh Empirical Methods in Natural Language Processing (EMNLP), 2020	1893	2020
Model-Agnostic Interpretability of Machine Learning MT Ribeiro, S Singh, C Guestrin ICML 2016 Workshop on Human Interpretability in Machine Learning, 2016	1341	2016
Calibrate before use: Improving few-shot performance of language models Z Zhao, E Wallace, S Feng, D Klein, S Singh International Conference on Machine Learning, 12697-12706, 2021	1318	2021
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList MT Ribeiro, T Wu, C Guestrin, S Singh arXiv preprint arXiv:2005.04118, 2020	1182	2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods D Slack, S Hilgard, E Jia, S Singh, H Lakkaraju Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 180-186, 2020	1051	2020
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs D Dua, Y Wang, P Dasigi, G Stanovsky, S Singh, M Gardner Annual Conference of the North American Chapter of the Association for …, 2019	943	2019
Universal Adversarial Triggers for Attacking and Analyzing NLP E Wallace, S Feng, N Kandpal, M Gardner, S Singh Empirical Methods in Natural Language Processing (EMNLP), 2019	941	2019
Knowledge enhanced contextual word representations ME Peters, M Neumann, RL Logan IV, R Schwartz, V Joshi, S Singh, ... arXiv preprint arXiv:1909.04164, 2019	872	2019
Generating Natural Adversarial Examples Z Zhao, D Dua, S Singh International Conference on Learning Representations (ICLR), 2018	737	2018
Semantically Equivalent Adversarial Rules for Debugging NLP Models MT Ribeiro, S Singh, C Guestrin Annual Meeting of the Association for Computational Linguistics (ACL), 2018	570	2018
Evaluating models’ local decision boundaries via contrast sets M Gardner, Y Artzi, V Basmov, J Berant, B Bogin, S Chen, P Dasigi, D Dua, ... Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020	495	2020
Injecting logical background knowledge into embeddings for relation extraction T Rocktäschel, S Singh, S Riedel Proceedings of the 2015 Human Language Technology Conference of the North …, 2015	315	2015
Do NLP Models Know Numbers? Probing Numeracy in Embeddings E Wallace, Y Wang, S Li, S Singh, M Gardner arXiv preprint arXiv:1909.07940, 2019	309	2019
Factorie: Probabilistic programming via imperatively defined factor graphs A McCallum, K Schultz, S Singh Advances in Neural Information Processing Systems 22, 1249-1257, 2009	293	2009
Entity linking via joint encoding of types, descriptions, and context N Gupta, S Singh, D Roth Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017	278	2017
Design challenges for entity linking X Ling, S Singh, DS Weld Transactions of the Association for Computational Linguistics 3, 315-328, 2015	265	2015
Impact of pretraining term frequencies on few-shot numerical reasoning Y Razeghi, RL Logan IV, M Gardner, S Singh Findings of the Association for Computational Linguistics: EMNLP 2022, 840-854, 2022	255*	2022
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling RL Logan IV, NF Liu, ME Peters, M Gardner, S Singh Proceedings of the 2019 Conference of the Association for Computational …, 2019	254	2019

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