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One of the key limitations of modern deep learning approaches lies in the amount of data required to train them.
Learning structural descriptions from examples
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Earlier work this paper cites.
The language of thought
Jerry A. Fodor · 1975
Earlier work this paper cites.
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David Marr and Herbert K. Nishihara · 1978
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Earlier work this paper cites.
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Earlier work this paper cites.
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