https://peertube.wtf/videos/watch/fbd5c8c3-b91f-4f11-9100-87159b34944c Abstract: In recent work on discrete neural networks, I considered such networks whose activation functions are polymorphisms of finite, discrete relational structures. The general framework I provided was not entirely categorical in nature but did provide a stepping stone to a categorical treatment of neural nets which are definitionally incapable of overfitting. In this talk I will outline how to view neural nets as categories of functors from certain multicategories to a target multicategory. Moreover, I will show that the results of my PhD thesis allow one to systematically define polymorphic learning algorithms for such neural nets in a manner applicable to any reasonable (read: functorial) finite data structure. Slides: https://aten.cool/documents/slides/aten_ny_category_theory_2023.pdf Tue, 18 Aug 2026 12:30:08 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://peertube.wtf https://peertube.wtf/lazy-static/avatars/45856119-df14-4b5f-bff8-4807ae2cfbd2.png https://peertube.wtf/videos/watch/fbd5c8c3-b91f-4f11-9100-87159b34944c All rights reserved, unless otherwise specified in the terms specified at https://peertube.wtf/about and potential licenses granted by each content's rightholder.