Joshua C. Peterson
TitleCited byYear
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
572015
Adapting deep network features to capture psychological representations
JC Peterson, JT Abbott, TL Griffiths
arXiv preprint arXiv:1608.02164, 2016
422016
Evaluating vector-space models of analogy
D Chen, JC Peterson, TL Griffiths
arXiv preprint arXiv:1705.04416, 2017
202017
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
162018
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
162016
Modelling student behavior using granular large scale action data from a MOOC
S Tang, JC Peterson, ZA Pardos
arXiv preprint arXiv:1608.04789, 2016
122016
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
122015
Modeling human categorization of natural images using deep feature representations
RM Battleday, JC Peterson, TL Griffiths
arXiv preprint arXiv:1711.04855, 2017
82017
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
72017
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
4*2019
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
42019
Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report.
JC Peterson, JT Abbott, TL Griffiths
IJCAI, 4934-4938, 2017
42017
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
32019
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
32018
Sampling from object and scene representations using deep feature spaces
J Peterson, K Aghi, J Suchow, A Ku, T Griffiths
Journal of Vision 18 (10), 403-403, 2018
12018
Learning a face space for experiments on human identity
JW Suchow, JC Peterson, TL Griffiths
arXiv preprint arXiv:1805.07653, 2018
12018
Evidence for the size principle in semantic and perceptual domains
JC Peterson, TL Griffiths
arXiv preprint arXiv:1705.03260, 2017
12017
Concept acquisition through meta-learning
E Grant, C Finn, J Peterson, J Abbott, S Levine, T Darrell, T Griffiths
NIPS Workshop on Cognitively Informed Artificial Intelligence, 2017
12017
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decision-Making
M Agrawal, JC Peterson, TL Griffiths
arXiv preprint arXiv:1910.07581, 2019
2019
Learning to generalize like humans using basic-level object labels
JC Peterson, P Soulos, A Nematzadeh, TL Griffiths
Journal of Vision 19 (10), 60a-60a, 2019
2019
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Articles 1–20