Материал из MachineLearning.
(Различия между версиями)
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| - | * Geometric deep learning
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| - | * Functional data analysis
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| - | * Applied mathematics for machine learning
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| - | ==Syllabus and goals==
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| - | ==Theme 1: ==
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| - | ===Message===
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| - | ===Basics===
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| - | ===Application===
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| - | ===Code===
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| - | https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf
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| - | ==Theme 1: ODE and flows==
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| - | *[https://papers.nips.cc/paper/2018/hash/69386f6bb1dfed68692a24c8686939b9-Abstract.html Neural Ordinary Differential Equations] (source paper and code)
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| - | *[https://en.wikipedia.org/wiki/Flow-based_generative_model W: Flow-based generative model]
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| - | *[https://deepgenerativemodels.github.io/notes/flow/ Flows at deepgenerativemodels.github.io]
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| - | *[https://habr.com/ru/company/ods/blog/442002/ Знакомство с Neural ODE на хабре]
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| - | Goes to BME
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| - | *[https://arxiv.org/pdf/1505.05770.pdf Variational Inference with Normalizing Flows (source paper)]
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| - | *[https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html Flow-based deep generative models]
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| - | ==Theme 1: PDE==
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| - | (after RBF)
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| - | ==Theme 1: High order splines==
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| - | ==Theme 1: Topological data analysis==
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| - | ==Theme 1: Homology versus homotopy==
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| - | [https://en.wikipedia.org/wiki/Homology_(mathematics) W: Homology]
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Версия 21:29, 6 сентября 2021
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Fundamental theorems
W: Inverse function theorem and Jacobian
