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Found 4 papers, 1 papers with code
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines
In order to take full advantage of these dependencies we consider a purely probabilistic setting in which the features imputation and multi-label classification problems are jointly solved.
Thermodynamics of Restricted Boltzmann Machines and related learning dynamics
In the non-linear regime, instead, the selected modes interact and eventually impose a matching of the order parameters to their empirical counterparts estimated from the data.
Spectral Dynamics of Learning Restricted Boltzmann Machines
This let us show how in the linear regime, in which the RBM is found to operate at the beginning of the training, the statistical properties of the data drive the selection of the unstable modes of the weight matrix.
