Joshua C. Peterson

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Citations	424	420
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20162017201820192020202113 45 72 99 167 24

Co-authors

Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
Ruairidh McLennan BattledayPhD Candidate, Princeton UniversityVerified email at princeton.edu
Joshua T. AbbottMelbourne School of Psychological SciencesVerified email at unimelb.edu.au
Zachary A. PardosAssociate Professor at UC BerkeleyVerified email at berkeley.edu
Rachit DubeyGraduate student, Princeton UniversityVerified email at berkeley.edu
Bernard GhanemAssociate Professor, King Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Stuart RussellProfessor of Computer Science, University of California, BerkeleyVerified email at cs.berkeley.edu
David D BourginUniversity of California BerkeleyVerified email at berkeley.edu
Dawn ChenUniversity of California, BerkeleyVerified email at berkeley.edu
Jordan SuchowStevens Institute of TechnologyVerified email at stevens.edu
Ming-Hsuan YangProfessor, University of California at Merced; Research Scientist, GoogleVerified email at ucmerced.edu
Aditya KhoslaCo-founder and CTO, PathAIVerified email at pathai.com
Olga RussakovskyAssistant Professor, Princeton UniversityVerified email at cs.princeton.edu
Ori PlonskyAssistant Professor, Technion - Israel Institute of TechnologyVerified email at technion.ac.il
Ido ErevProfessor of Behavioral Science, TechnionVerified email at tx.technion.ac.il
Evan C CarterU.S. Army Research LaboratoryVerified email at mail.mil
Moshe TennenholtzVerified email at ie.technion.ac.il
Eyal ErtAssociate Professor, The Hebrew University of JerusalemVerified email at mail.huji.ac.il
Aida NematzadehResearch Scientist, DeepMindVerified email at google.com
Martina RauDepartment of Educational Psychology, University of Wisconsin - MadisonVerified email at wisc.edu

Joshua C. Peterson

Princeton University

Verified email at princeton.edu - Homepage

Cognitive Science Machine Learning Artificial Intelligence


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
What makes an object memorable? R Dubey, J Peterson, A Khosla, MH Yang, B Ghanem Proceedings of the ieee international conference on computer vision, 1089-1097, 2015	79	2015
Adapting deep network features to capture psychological representations JC Peterson, JT Abbott, TL Griffiths arXiv preprint arXiv:1608.02164, 2016	54	2016
Evaluating vector-space models of analogy D Chen, JC Peterson, TL Griffiths arXiv preprint arXiv:1705.04416, 2017	45	2017
Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations JC Peterson, JT Abbott, TL Griffiths Cognitive Science 42 (8), 2648-2669, 2018	40	2018
Cognitive model priors for predicting human decisions J Peterson, D Bourgin, D Reichman, S Russell, T Griffiths International Conference on Machine Learning, 5133-5141, 2019	28*	2019
Human uncertainty makes classification more robust JC Peterson, RM Battleday, TL Griffiths, O Russakovsky Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019	28	2019
Deep neural networks and how they apply to sequential education data S Tang, JC Peterson, ZA Pardos Proceedings of the Third (2016) ACM Conference on Learning@ Scale, 321-324, 2016	27	2016
Predicting human decisions with behavioral theories and machine learning O Plonsky, R Apel, E Ert, M Tennenholtz, D Bourgin, JC Peterson, ... arXiv preprint arXiv:1904.06866, 2019	18	2019
Understanding Student Success in Chemistry using Gaze Tracking & Pupillometry J Peterson, Z Pardos, M Rau, A Swigart, C Gerber, J McKinsey Artificial Intelligence in Education, 2015	15	2015
Modeling human categorization of natural images using deep feature representations RM Battleday, JC Peterson, TL Griffiths arXiv preprint arXiv:1711.04855, 2017	13	2017
Modelling student behavior using granular large scale action data from a MOOC S Tang, JC Peterson, ZA Pardos arXiv preprint arXiv:1608.04789, 2016	13	2016
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report. JC Peterson, JT Abbott, TL Griffiths IJCAI, 4934-4938, 2017	10	2017
Predictive modelling of student behaviour using granular large-scale action data S Tang, J Peterson, Z Pardos The Handbook of Learning Analytics, 223-233, 2017	10	2017
Capturing human categorization of natural images at scale by combining deep networks and cognitive models RM Battleday, JC Peterson, TL Griffiths arXiv preprint arXiv:1904.12690, 2019	8	2019
Learning hierarchical visual representations in deep neural networks using hierarchical linguistic labels JC Peterson, P Soulos, A Nematzadeh, TL Griffiths arXiv preprint arXiv:1805.07647, 2018	7	2018
Using machine learning to guide cognitive modeling: A case study in moral reasoning M Agrawal, JC Peterson, TL Griffiths arXiv preprint arXiv:1902.06744, 2019	6	2019
Human uncertainty makes classification more robust. 2019 IEEE JC Peterson, RM Battleday, TL Griffiths, O Russakovsky CVF International Conference on Computer Vision (ICCV), 9616-9625, 2019	5	2019
Psychological and musical factors underlying engagement with unfamiliar music P Janata, J Peterson, C Ngan, B Keum, H Whiteside, S Ran Music Perception: An Interdisciplinary Journal 36 (2), 175-200, 2018	3	2018
Learning a face space for experiments on human identity JW Suchow, JC Peterson, TL Griffiths arXiv preprint arXiv:1805.07653, 2018	3	2018
Learning deep taxonomic priors for concept learning from few positive examples E Grant, JC Peterson, TL Griffiths Proceedings of the Annual Conference of the Cognitive Science Society, 2019	2	2019

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