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Human categorization is one of the most important and successful targets of cognitive modeling in psychology, yet decades of development and assessment of competing models have been contingent on small sets of simple, artificial experimental stimuli.
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Palmeri, T. J. & Nosofsky, R. M · 2001
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Zaki, S. R., Nosofsky, R. M., Stanton, R. D. & Cohen, A. L · 2003
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Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2012
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Agrawal, P., Stansbury, D., Malik, J. & Gallant, J. L · 2014
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Caffe: Convolutional architecture for fast feature embedding
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Deep learning
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Distinctive image features from scale-invariant keypoints
Lowe, D. G · 2004
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Cohen, H. & Lefebvre, C · 2005
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Griffiths, T. L., Canini, K., Sanborn, A. & Navarro, D · 2007
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Krizhevsky, A. & Hinton, G · 2009
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Buhrmester, M., Kwang, T. & Gosling, S. D · 2011
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LeCun, Y., Bengio, Y. & Hinton, G · 2015
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Deep neural networks predict category typicality ratings for images
Lake, B., Zaremba, W., Fergus, R. & Gureckis, T. M · 2015
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Densely connected convolutional networks
Huang, G., Liu, Z. & Weinberger, K. Q · 2016
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Adversarially learned inference
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On learning natural-science categories that violate the family-resemblance principle
Nosofsky, R. M., Sanders, C. A., Gerdom, A., Douglas, B. J. & McDaniel, M. A · 2017
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Evaluating (and improving) the correspondence between deep neural networks and human representations
Peterson, J. C., Abbott, J. T. & Griffiths, T. L · 2018
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Using deep learning representations of complex natural stimuli as input to psychological models of classification
Sanders, C. A. & Nosofsky, R. M · 2018
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