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nLab > Latest Changes: machine learning

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    • CommentRowNumber1.
    • CommentAuthorDavid_Corfield
    • CommentTimeMar 4th 2021

    A stub for now.

    v1, current

    • CommentRowNumber2.
    • CommentAuthorUrs
    • CommentTimeApr 20th 2021

    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

    diff, v2, current

    • CommentRowNumber3.
    • CommentAuthorUrs
    • CommentTimeJan 13th 2023

    added this pointer:

    diff, v17, current

    • CommentRowNumber4.
    • CommentAuthorUrs
    • CommentTimeApr 7th 2023

    added pointer to:

    diff, v19, current

    • CommentRowNumber5.
    • CommentAuthorzskoda
    • CommentTimeApr 7th 2023

    • Shai Shalev-Shwartz, Shai Ben-David, Understanding machine learning: from theory to algorithms, webpage Cambridge University Press 2014

    • Ian Goodfellow, Y. Bengio, A. Courville, Deep learning, pdf MIT Press 2016

    diff, v20, current

    • CommentRowNumber6.
    • CommentAuthorzskoda
    • CommentTimeApr 12th 2023

    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

    diff, v23, current

    • CommentRowNumber7.
    • CommentAuthorUrs
    • CommentTimeApr 17th 2023

    added pointer to this exposition:

    diff, v24, current

    • CommentRowNumber8.
    • CommentAuthorzskoda
    • CommentTimeJul 25th 2023

    On transformers and large language models (LLM)

    • Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, Attention is all you need, in Advances in Neural Information Processing Systems 30 (NIPS 2017) pdf

    (by the way, at google scholar, at the moment, “cited by 82841”)

    and recent intro survey

    diff, v25, current

    • CommentRowNumber9.
    • CommentAuthorzskoda
    • CommentTimeSep 19th 2023

    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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