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Human brains respond to semantic features of presented stimuli with different neurons.
Principles of mathematical analysis , volume 3
Rudin, W. et al · 1976
Earlier work this paper cites.
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Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., and Turner, R · 1992
Earlier work this paper cites.
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Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A · 2001
Earlier work this paper cites.
Functional magnetic resonance imaging , volume 1
Huettel, S. A., Song, A. W., McCarthy, G., et al · 2004
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Lee, J., Cho, K., and Kiela, D · 2019
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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