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Found 11 papers, 4 papers with code
Inferring effective couplings with Restricted Boltzmann Machines
Generative models offer a direct way of modeling complex data.
Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling
Moreover, we show that this sampling technique can also be used to improve the computation of the log-likelihood gradient during training, leading to dramatic improvements in training RBMs with artificial clustered datasets.
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines
In this work, we show that this mixing time plays a crucial role in the dynamics and stability of the trained model, and that RBMs operate in two well-defined regimes, namely equilibrium and out-of-equilibrium, depending on the interplay between this mixing time of the model and the number of steps, $k$, used to approximate the gradient.
Restricted Boltzmann Machine, recent advances and mean-field theory
This review deals with Restricted Boltzmann Machine (RBM) under the light of statistical physics.
Dynamic Time Lag Regression: Predicting What & When
DTLR differs from mainstream regression and from sequence-to-sequence learning in two respects: firstly, no ground truth (e. g., pairs of associated sub-sequences) is available; secondly, the cause signal contains much information irrelevant to the effect signal (the solar magnetic field governs the solar wind propagation in the heliosphere, of which the Earth's magnetosphere is but a minuscule region).
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.
Gaussian-Spherical Restricted Boltzmann Machines
We consider a special type of Restricted Boltzmann machine (RBM), namely a Gaussian-spherical RBM where the visible units have Gaussian priors while the vector of hidden variables is constrained to stay on an ${\mathbbm L}_2$ sphere.
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.
Using Latent Binary Variables for Online Reconstruction of Large Scale Systems
We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable.
Traffic data reconstruction based on Markov random field modeling
We consider the traffic data reconstruction problem.
