Joshua C. Peterson
Joshua C. Peterson
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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
Adapting deep network features to capture psychological representations
JC Peterson, JT Abbott, TL Griffiths
arXiv preprint arXiv:1608.02164, 2016
Evaluating vector-space models of analogy
D Chen, JC Peterson, TL Griffiths
arXiv preprint arXiv:1705.04416, 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
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
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
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
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
Capturing human categorization of natural images by combining deep networks and cognitive models
RM Battleday, JC Peterson, TL Griffiths
Nature communications 11 (1), 1-14, 2020
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
Modeling human categorization of natural images using deep feature representations
RM Battleday, JC Peterson, TL Griffiths
arXiv preprint arXiv:1711.04855, 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
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
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
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
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
Learning a face space for experiments on human identity
JW Suchow, JC Peterson, TL Griffiths
arXiv preprint arXiv:1805.07653, 2018
Parallelograms revisited: Exploring the limitations of vector space models for simple analogies
JC Peterson, D Chen, TL Griffiths
Cognition 205, 104440, 2020
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
Capturing human category representations by sampling in deep feature spaces
JC Peterson, JW Suchow, K Aghi, AY Ku, TL Griffiths
arXiv preprint arXiv:1805.07644, 2018
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