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• W3172322015 abstract "Deep Matrix Factorization (DMF) is an emerging approach to the problem of matrix completion. Recent works have established that gradient descent applied to a DMF model induces an implicit regularization on the rank of the recovered matrix. In this work we interpret the DMF model through the lens of spectral geometry. This allows us to incorporate explicit regularization without breaking the DMF structure, thus enjoying the best of both worlds. In particular, we focus on matrix completion problems with underlying geometric or topological relations between the rows and/or columns. Such relations are prevalent in matrix completion problems that arise in many applications, such as recommender systems and drug-target interaction. Our contributions enable DMF models to exploit these relations, and make them competitive on real benchmarks, while exhibiting one of the first successful applications of deep linear networks." @default.
• W3172322015 created "2021-06-22" @default.
• W3172322015 creator A5025410418 @default.
• W3172322015 creator A5041096814 @default.
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• W3172322015 date "2019-11-17" @default.
• W3172322015 modified "2023-09-27" @default.
• W3172322015 title "Spectral Geometric Matrix Completion" @default.
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