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pro vyhledávání: '"Cohen, J��r��my E."'
The ability of deep neural networks to learn complex data relations and representations is established nowadays, but it generally relies on large sets of training data. This work explores a "piece-specific" autoencoding scheme, in which a low-dimensi
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https://explore.openaire.eu/search/publication?articleId=doi_________::323a1ac0d45104cab6368f46638df495
Autor:
Cohen, J��r��my E., Gillis, Nicolas
Sparse component analysis (SCA), also known as complete dictionary learning, is the following problem: Given an input matrix $M$ and an integer $r$, find a dictionary $D$ with $r$ columns and a matrix $B$ with $k$-sparse columns (that is, each column
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d155630e61090a2c7002d5814c1474d4
Autor:
Cohen, J��r��my E., Gillis, Nicolas
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of spa
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https://explore.openaire.eu/search/publication?articleId=doi_________::d3832b15ad0adfdc8808f05e802347cb