Journal article
A mathematical theory of semantic development in deep neural networks
- Abstract:
-
An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: What are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Bibliographic Details
- Publisher:
- National Academy of Sciences Publisher's website
- Journal:
- Proceedings of the National Academy of Sciences Journal website
- Volume:
- 116
- Issue:
- 23
- Pages:
- 11537-11546
- Publication date:
- 2019-05-17
- Acceptance date:
- 2019-04-09
- DOI:
- EISSN:
-
1091-6490
- ISSN:
-
0027-8424
- Pmid:
-
31101713
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:999449
- UUID:
-
uuid:675edddf-32b3-4830-b781-6b47e02a3a8c
- Local pid:
- pubs:999449
- Source identifiers:
-
999449
- Deposit date:
- 2019-07-04
Terms of use
- Copyright holder:
- Saxe et al
- Copyright date:
- 2019
- Notes:
- © 2019 The Author(s). Published under the PNAS license. This is the accepted manuscript version of the article. The final version is available online from National Academy of Sciences at: https://doi.org/10.1073/pnas.1820226116
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