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Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images.
Sorting system with nu-line sorting switch
O’connor, D., & Nelson, R. (1962) · 1962
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A ‘complexity level’analysis of immediate vision
Tsotsos, J. K. (1988) · 1988
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Lower bounds for sorting networks
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Long short-term memory
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Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
Hahnloser, R. H., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J., & Seung, H. S. (2000) · 2000
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The notorious difficulty of comparing human and machine perception
Funke, C. M., Borowski, J., Stosio, K., Brendel, W., Wallis, T. S., & Bethge, M. (2020) · 2004
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Comparing machines and humans on a visual categorization test
Fleuret, F., Li, T., Dubout, C., Wampler, E. K., Yantis, S., & Geman, D. (2011) · 2011
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016
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25 years of cnns: Can we compare to human abstraction capabilities?
Stabinger, S., Rodríguez-Sánchez, A., & Piater, J. (2016) · 2016
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Not-so-clevr: learning same–different relations strains feedforward neural networks
Kim, J., Ricci, M., & Serre, T. (2018) · 2018
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Cornet: modeling the neural mechanisms of core object recognition
Kubilius, J., Schrimpf, M., Nayebi, A., Bear, D., Yamins, D. L., & DiCarlo, J. J. (2018) · 2018
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Same-different problems strain convolutional neural networks
Ricci, M., Kim, J., & Serre, T. (2018) · 2018
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Testing deep neural networks on the same-different task
Messina, N., Amato, G., Carrara, F., Falchi, F., & Gennaro, C. (2019) · 2019
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New deep learning optimizer, ranger: Synergistic combination of radam + lookahead for the best of both
Wright, L. (2019) · 2019
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