Fretful
发表于 2025-3-25 04:06:09
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Bmd955
发表于 2025-3-25 09:49:43
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hemophilia
发表于 2025-3-25 12:19:33
Algorithmic Learning Theory978-3-319-24486-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
Soliloquy
发表于 2025-3-25 17:07:24
https://doi.org/10.1007/978-3-8350-9248-8 either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.
garrulous
发表于 2025-3-25 23:26:09
,Der Kräfteausgleich im Gebirge,. Existing methods for the problem either rely on measurement operators such as random element-wise sampling which cannot recover arbitrary low-rank matrices or require the measurement operator to satisfy the Restricted Isometry Property (RIP). However, RIP based linear operators are generally full
很是迷惑
发表于 2025-3-26 03:20:09
https://doi.org/10.1007/978-3-642-91890-2 a computationally and statistically efficient parameter estimation method for a wide class of latent variable models—including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation—which exploits a certain tensor structure in their low-order observable moments (typically, o
Flatter
发表于 2025-3-26 06:32:20
,Der Kräfteausgleich im Gebirge,during the overall learning history. Priced learning relaxes this constraint on the update of memory by imposing some price on the updates of the memory – depending on the current datum – and requiring that the overall sum of the costs incurred has to be finite. There are priced-learnable classes wh
流浪
发表于 2025-3-26 12:07:30
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considerable
发表于 2025-3-26 13:55:10
https://doi.org/10.1007/978-3-8350-9248-8ultivalued dependencies. In this work, we study the learnability of multivalued dependency formulas (MVDF), which correspond to the logical theory behind multivalued dependencies. As we explain, MVDF lies between propositional Horn and 2-Quasi-Horn. We prove that MVDF is polynomially learnable in An
Flat-Feet
发表于 2025-3-26 16:58:31
https://doi.org/10.1007/978-3-8350-9248-8 either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.