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- W4320165641 abstract "This article provides a concise overview of some of the recent advances in the application of rough path theory to machine learning. Controlled differential equations (CDEs) are discussed as the key mathematical model to describe the interaction of a stream with a physical control system. A collection of iterated integrals known as the signature naturally arises in the description of the response produced by such interactions. The signature comes equipped with a variety of powerful properties rendering it an ideal feature map for streamed data. We summarise recent advances in the symbiosis between deep learning and CDEs, studying the link with RNNs and culminating with the Neural CDE model. We concluded with a discussion on signature kernel methods." @default.
- W4320165641 created "2023-02-13" @default.
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- W4320165641 date "2023-02-09" @default.
- W4320165641 modified "2023-09-29" @default.
- W4320165641 title "New directions in the applications of rough path theory" @default.
- W4320165641 doi "https://doi.org/10.48550/arxiv.2302.04586" @default.
- W4320165641 hasPublicationYear "2023" @default.
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