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Joshua T. Abbott
Joshua T. Abbott
Adobe
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Random walks on semantic networks can resemble optimal foraging
JT Abbott, JL Austerweil, TL Griffiths
Psychological Review 122 (3), 558-569, 2015
2022015
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
1702018
Biological origins of color categorization
AE Skelton, G Catchpole, JT Abbott, JM Bosten, A Franklin
Proceedings of the National Academy of Sciences 114 (21), 5545-5550, 2017
1182017
Adapting Deep Network Features to Capture Psychological Representations
JC Peterson, JT Abbott, TL Griffiths
Proceedings of the 38th Annual Conference of the Cognitive Science Society …, 2016
952016
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
Y Jia, J Abbott, J Austerweil, T Griffiths, T Darrell
Advances in Neural Information Processing Systems 26, 2013
802013
Human memory search as a random walk in a semantic network
J Abbott, J Austerweil, T Griffiths
Advances in Neural Information Processing Systems 25, 3050-3058, 2012
78*2012
Focal colors across languages are representative members of color categories
JT Abbott, TL Griffiths, T Regier
Proceedings of the National Academy of Sciences 113 (40), 11178-11183, 2016
692016
Exploring the influence of particle filter parameters on order effects in causal learning
JT Abbott, TL Griffiths
Proceedings of the 33rd Annual Conference of the Cognitive Science Society …, 2011
422011
Empirical Evidence for Markov Chain Monte Carlo in Memory Search
DD Bourgin, JT Abbott, TL Griffiths, KA Smith, E Vul
Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014
382014
Constructing a hypothesis space from the Web for large-scale Bayesian word learning
JT Abbott, JL Austerweil, TL Griffiths
Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012
302012
Approximating Bayesian inference with a sparse distributed memory system
JT Abbott, JB Hamrick, TL Griffiths
Proceedings of the 35th Annual Conference of the Cognitive Science Society, 2013
222013
Testing a Bayesian Measure of Representativeness Using a Large Image Database
JT Abbott, KA Heller, Z Ghahramani, TL Griffiths
Advances in Neural Information Processing Systems 24, 2321-2329, 2011
152011
Exploring human cognition using large image databases
TL Griffiths, JT Abbott, AS Hsu
Topics in cognitive science 8 (3), 569-588, 2016
112016
Predicting focal colors with a rational model of representativeness
JT Abbott, T Regier, TL Griffiths
Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012
102012
Birds and Words: Exploring environmental influences on folk categorization
JT Abbott, C Kemp
Proceedings of the 42nd Annual Conference of the Cognitive Science Society …, 2020
32020
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, 34, 2017
32017
Recommendation as Generalization: Evaluating Cognitive Models In the Wild
DD Bourgin, JT Abbott, TL Griffiths
Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018
22018
Visually-Grounded Bayesian Word Learning
Y Jia, J Abbott, J Austerweil, T Griffiths, T Darrell
EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS …, 2012
12012
Recommendation as generalization: Using big data to evaluate cognitive models.
DD Bourgin, JT Abbott, TL Griffiths
Journal of Experimental Psychology: General 150 (7), 1398, 2021
2021
Leveraging deep neural networks to capture psychological representations
JC Peterson, JT Abbott, TL Griffiths
arXiv preprint arXiv:1706.02417, 2017
2017
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Artículos 1–20