Andreas Stuhlmüller

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Noah D. GoodmanStanford UniversityCorreu electrònic verificat a stanford.edu
Owain EvansAssociate, CHAI, UC BerkeleyCorreu electrònic verificat a philosophy.ox.ac.uk
Joshua B. TenenbaumMITCorreu electrònic verificat a mit.edu
David WingateAssociate professor of Computer Science, Brigham Young UniversityCorreu electrònic verificat a cs.byu.edu
Tomer UllmanAssistant Professor, HarvardCorreu electrònic verificat a fas.harvard.edu
William SaundersOpenAICorreu electrònic verificat a cs.toronto.edu
Girish SastryIndependentCorreu electrònic verificat a openai.com
Samuel GershmanProfessor, Harvard UniversityCorreu electrònic verificat a fas.harvard.edu
Demi GuoHarvard UniversityCorreu electrònic verificat a college.harvard.edu
Ishita DasguptaSenior Research Scientist, DeepMindCorreu electrònic verificat a google.com
Robert D. HawkinsStanford UniversityCorreu electrònic verificat a stanford.edu
David AbelDeepMind / University of EdinburghCorreu electrònic verificat a deepmind.com
Eli LiflandCorreu electrònic verificat a sage-future.org
Abhishek ThakurHugging FaceCorreu electrònic verificat a huggingface.co
Lewis TunstallHugging FaceCorreu electrònic verificat a itp.unibe.ch
Michael NoetelUniversity of QueenslandCorreu electrònic verificat a uq.edu.au
Jess RiedelNTT Research - Physics & Informatics LabCorreu electrònic verificat a ntt-research.com
Emmie HineYale UniversityCorreu electrònic verificat a yale.edu
Judith DegenAssociate professor, Stanford University (Linguistics)Correu electrònic verificat a stanford.edu
Daniel RitchieBrown UniversityCorreu electrònic verificat a brown.edu

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Andreas Stuhlmüller

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Correu electrònic verificat a elicit.com - Pàgina d'inici

Language Models Machine Learning Probabilistic Programming Cognitive Science AI


Títol Ordena per cites Ordena per any Ordena per títol	Citada per Citada per	Any
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022	1291	2022
Knowledge and implicature: Modeling language understanding as social cognition ND Goodman, A Stuhlmüller Topics in cognitive science 5 (1), 173-184, 2013	555	2013
The Design and Implementation of Probabilistic Programming Languages ND Goodman, A Stuhlmüller http://dippl.org, 2014	390	2014
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention W Saunders, G Sastry, A Stuhlmueller, O Evans Proceedings of the 17th International Conference on Autonomous Agents and …, 2018	333	2018
Lightweight implementations of probabilistic programming languages via transformational compilation D Wingate, A Stuhlmüller, ND Goodman International Conference on Artificial Intelligence and Statistics, 770-778, 2011	219	2011
Learning the Preferences of Ignorant, Inconsistent Agents O Evans, A Stuhlmüller, ND Goodman Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-2016), 2016	154	2016
Evaluating Compositionality in Sentence Embeddings I Dasgupta, D Guo, A Stuhlmüller, SJ Gershman, ND Goodman arXiv preprint arXiv:1802.04302, 2018	140	2018
Learning Stochastic Inverses A Stuhlmüller, J Taylor, N Goodman Advances in Neural Information Processing Systems, 3048-3056, 2013	127	2013
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs A Stuhlmüller, ND Goodman Cognitive Systems Research 28, 80-99, 2014	96	2014
Agent-Agnostic Human-in-the-Loop Reinforcement Learning D Abel, J Salvatier, A Stuhlmüller, O Evans Future of Interactive Learning Machines Workshop at NIPS 2016, 2016	86	2016
Learning physical parameters from dynamic scenes TD Ullman, A Stuhlmüller, ND Goodman, JB Tenenbaum Cognitive psychology 104, 57-82, 2018	73	2018
RAFT: A Real-World Few-Shot Text Classification Benchmark N Alex, E Lifland, L Tunstall, A Thakur, P Maham, CJ Riedel, E Hine, ... arXiv preprint arXiv:2109.14076, 2021	70	2021
Why do you ask? Good questions provoke informative answers. RXD Hawkins, A Stuhlmüller, J Degen, ND Goodman Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015	53	2015
Learning physics from dynamical scenes T Ullman, A Stuhlmüller, N Goodman, JB Tenenbaum Proceedings of the 36th Annual Conference of the Cognitive Science Society …, 2014	43*	2014
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching D Ritchie, A Stuhlmüller, ND Goodman Proceedings of the 19th International Conference on Artificial Intelligence …, 2016	41	2016
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference D Wingate, ND Goodman, A Stuhlmüller, JM Siskind Advances in Neural Information Processing Systems, 2011	41	2011
Learning Structured Generative Concepts A Stuhlmüller, JB Tenenbaum, ND Goodman Proceedings of the Thirty-Second Annual Conference of the Cognitive Science …, 2010	38	2010
Learning the Preferences of Bounded Agents O Evans, A Stuhlmüller, ND Goodman Advances in Neural Information Processing Systems (Bounded Optimality Workshop), 2015	37	2015
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs A Stuhlmüller, ND Goodman Second Statistical Relational AI workshop at UAI 2012 (StaRAI-12), 2012	36	2012
Inducing Probabilistic Programs by Bayesian Program Merging I Hwang, A Stuhlmüller, ND Goodman Arxiv preprint arXiv:1110.5667, 2011	31	2011

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