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Vanilla 1.1.10 is a product of Lussumo. More Information: Documentation, Community Support.
cross-linked with topological data analysis, added pointer to Wikipedia (so that there is at least some reference) and added pointer to kernel method for which I will now create a stub
added this pointer:
added pointer to:
Sumio Watanabe, Algebraic geometry and statistical learning theory, CRC Press (2009) [doi:10.1017/CBO9780511800474]
Sumio Watanabe, Mathematical theory of Bayesian statistics, Cambridge University Press (2018) [ISBN:9780367734817, pdf]
On a definition of artificial general intelligence
S. Legg, M. Hutter, Universal intelligence: a definition of machine intelligence, Minds & Machines 17, 391–444 (2007) doi
M. Hutter, Universal artificial intelligence: sequential decisions based on algorithmic probability, Springer 2005; book presentation pdf
Shane Legg, Machine super intelligence, PhD thesis, 2008 pdf
added pointer to this exposition:
On transformers and large language models (LLM)
(by the way, at google scholar, at the moment, “cited by 82841”)
and recent intro survey
Tomorrow an interesting online talk in the area, in categorical approach:
https://researchseminars.org/talk/CompAlg/26
Fundamental Components of Deep Learning: A category-theoretic approach
Bruno Gavranović (Strathclyde)
Wed Sep 20
Abstract: Deep learning, despite its remarkable achievements, is still a young field. Like the early stages of many scientific disciplines, it is permeated by ad-hoc design decisions. From the intricacies of the implementation of backpropagation, through new and poorly understood phenomena such as double descent, scaling laws or in-context learning, to a growing zoo of neural network architectures - there are few unifying principles in deep learning, and no uniform and compositional mathematical foundation. In this talk I’ll present a novel perspective on deep learning by utilising the mathematical framework of category theory. I’ll identify two main conceptual components of neural networks, report on progress made throughout last years by the research community in formalising them, and show how they’ve been used to describe backpropagation, architectures, and supervised learning in general, shedding a new light on the existing field.
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