Joseph Austerweil

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Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
Joshua T. AbbottMelbourne School of Psychological SciencesVerified email at unimelb.edu.au
Fiery CushmanAssociate Prof., Psychology, Harvard UniversityVerified email at fas.harvard.edu
Joshua B. TenenbaumMITVerified email at mit.edu
Jeffrey C. ZemlaSyracuse UniversityVerified email at syr.edu
Micha ElsnerAssistant Professor of Linguistics, The Ohio State UniversityVerified email at ling.ohio-state.edu
Eugene CharniakProfessor of Computer Science, Brown UniversityVerified email at brown.edu
Max Kleiman-WeinerHarvard & MITVerified email at mit.edu
Yoed N. KenettSenior Lecturer, Technion - Israel Institute of TechnologyVerified email at technion.ac.il
Yangqing JiaVP, Alibaba GroupVerified email at daggerfs.com
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
Amy GreenwaldProfessor of Computer Science, Brown UniversityVerified email at brown.edu
Bertram MalleBrown UniversityVerified email at brown.edu
Matthias ScheutzProfessor of Computer Science and Cognitive Science, Tufts UniversityVerified email at tufts.edu
Emily CibelliData Scientist, PicnicHealthVerified email at berkeley.edu
Cohen Priva, UrielBrown UniversityVerified email at brown.edu
Samuel GershmanProfessor, Harvard UniversityVerified email at fas.harvard.edu
Stephen E. PalmerU.C. BerkeleyVerified email at berkeley.edu
Kwang-Sung JunThe University of ArizonaVerified email at cs.arizona.edu
Mowafak AllahamUniversity of Illinois at ChicagoVerified email at uic.edu

Joseph Austerweil

Assistant Professor at University of Wisconsin-Madison

Verified email at wisc.edu - Homepage

Computational cognitive science Inductive Inference knowledge representation Categorization Nonparametric Bayesian Methods


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Random Walks on Semantic Networks Can Resemble Optimal Foraging. JT Abbott, JL Austerweil, TL Griffiths Psychological Review 122 (3), 558-559, 2015	113	2015
Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction M Kleiman-Weiner, MK Ho, JL Austerweil, ML Littman, JB Tenenbaum COGSCI, 2016	112	2016
A unified local and global model for discourse coherence M Elsner, J Austerweil, E Charniak Human Language Technologies 2007: The Conference of the North American …, 2007	82	2007
Multilevel coarse-to-fine PCFG parsing E Charniak, M Johnson, M Elsner, J Austerweil, D Ellis, I Haxton, C Hill, ... Proceedings of the Human Language Technology Conference of the NAACL, Main …, 2006	78	2006
Seeking Confirmation Is Rational for Deterministic Hypotheses JL Austerweil, TL Griffiths Cognitive Science 35, 499-526, 2011	74	2011
The Sapir-Whorf hypothesis and probabilistic inference: Evidence from the domain of color E Cibelli, Y Xu, JL Austerweil, TL Griffiths, T Regier PloS one 11 (7), e0158725, 2016	65	2016
The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color E Cibelli, Y Xu, JL Austerweil, TL Griffiths, T Regier PLOS ONE 11 (7), e0158725, 2016	65	2016
Showing versus doing: Teaching by demonstration MK Ho, M Littman, J MacGlashan, F Cushman, JL Austerweil Advances In Neural Information Processing Systems, 3027-3035, 2016	64	2016
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Y Jia, J Abbott, J Austerweil, T Griffiths, T Darrell NIPS, 2013	59	2013
Analyzing the history of Cognition using topic models UC Priva, JL Austerweil Cognition 135, 4-9, 2015	54	2015
Human memory search as a random walk in a semantic network JT Abbott, JL Austerweil, TL Griffiths Advances in Neural Information Processing Systems (Vol. 25), 2012	54*	2012
Networks of Social and Moral Norms in Human and Robot Agents BF Malle, M Scheutz, JL Austerweil A World with Robots, 3-17, 2017	48	2017
New perspectives on the aging lexicon DU Wulff, S De Deyne, MN Jones, R Mata, Aging Lexicon Consortium Trends in cognitive sciences 23 (8), 686-698, 2019	46	2019
A nonparametric Bayesian framework for constructing flexible feature representations. JL Austerweil, TL Griffiths Psychological Review 120 (4), 817-851, 2013	40	2013
Feature-based Joint Planning and Norm Learning in Collaborative Games MK Ho, J MacGlashan, A Greenwald, ML Littman, EM Hilliard, C Trimbach, ... Annual Conference of the Cognitive Science Society, 2016	38	2016
Structure and flexibility in Bayesian models of cognition JL Austerweil, SJ Gershman, JB Tenenbaum, TL Griffiths Oxford Handbook of Computational and Mathematical Psychology. Oxford …, 2015	35	2015
Estimating semantic networks of groups and individuals from fluency data JC Zemla, JL Austerweil Computational Brain & Behavior 1 (1), 36-58, 2018	34	2018
Examining Search Processes in Low and High Creative Individuals with Random Walks YN Kenett, JL Austerweil Cognitive Conference Society, 2016	33	2016
Teaching with Rewards and Punishments: Reinforcement or Communication? MK Ho, ML Littman, F Cushman, JL Austerweil Annual Proceedings of the 37th Cognitive Science Society Meeting, 2015	30	2015
Constructing a hypothesis space from the Web for large-scale Bayesian word learning JT Abbott, JL Austerweil, TL Griffiths 34th Annual Meeting of the Cognitive Science Society, 2012	26	2012

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