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Found 4 papers, 1 papers with code
The Decimation Scheme for Symmetric Matrix Factorization
Matrix factorization is an inference problem that has acquired importance due to its vast range of applications that go from dictionary learning to recommendation systems and machine learning with deep networks.
Fundamental limits of overparametrized shallow neural networks for supervised learning
We carry out an information-theoretical analysis of a two-layer neural network trained from input-output pairs generated by a teacher network with matching architecture, in overparametrized regimes.
Matrix factorization with neural networks
Matrix factorization is an important mathematical problem encountered in the context of dictionary learning, recommendation systems and machine learning.
Bayes-optimal limits in structured PCA, and how to reach them
To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise.
