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Titlebook: Representation Discovery using Harmonic Analysis; Sridhar Mahadevan Book 2008 Springer Nature Switzerland AG 2008

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樓主: frustrate
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發(fā)表于 2025-3-25 04:50:30 | 只看該作者
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Sridhar Mahadevanhinder the data acquisition process. Current medical image synthesis techniques, often designed for specific tasks or modalities, exhibit diminished performance when confronted with heterogeneous-source MRI data. Here?we introduce a .ext-guided .niversal .R image .thesis (TUMSyn) generalist model to
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Overview,an be used to generate in a unique manner all the remaining elements. This simple notion of representation turns out to be surprisingly rich both theoretically—it includes representations in finite-dimensional linear algebra [110], group theory [51], and infinite-dimensional function spaces [37]—as
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Book 2008s book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete
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發(fā)表于 2025-3-26 19:13:01 | 只看該作者
1939-4608 rete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete 978-3-031-00418-6978-3-031-01546-5Series ISSN 1939-4608 Series E-ISSN 1939-4616
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