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- W4327946472 abstract "Machine learning (ML) in its current form implies that the answer to any problem can be well approximated by a function of a very peculiar form: a specially adjusted iteration of Heaviside theta-functions. It is natural to ask whether the answers to questions that we already know can be naturally represented in this form. We provide elementary and yet nonevident examples showing that this is indeed possible, and suggest to look for a systematic reformulation of existing knowledge in an ML-consistent way. The success or failure of these attempts can shed light on a variety of problems, both scientific and epistemological." @default.
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- W4327946472 date "2023-03-01" @default.
- W4327946472 modified "2023-10-18" @default.
- W4327946472 title "Machine learning of the well-known things" @default.
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- W4327946472 doi "https://doi.org/10.1134/s0040577923030091" @default.
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