Modelling conditional probabilities with Riemann-Theta Boltzmann Machines
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of the Riemann-Theta Boltzmann machine modelling the original probability density function. Therefore the conditional densities can be directly inferred from the Riemann-Theta Boltzmann machine.
Code (1)
Similar Papers 제목 키워드 기반
Riemann-Theta Boltzmann Machine
A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can…
Product Jacobi-Theta Boltzmann machines with score matching
The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques. Successful applications can be obtained using models inspired by the Boltz…
Sampling the Riemann-Theta Boltzmann Machine
We show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a gaussian mixture model consisting of an infinite number of component multi-variate gaussians. The weigh…
Conditional probability calculation using restricted Boltzmann machine with application to system identification
There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extra…
Parameter Estimation for Generalized Low-Rank Matrix Sensing by Learning on Riemannian Manifolds
We prove convergence guarantees for generalized low-rank matrix sensing -- i.e., where matrix sensing where the observations may be passed through some nonlinear link function. We focus on local convergence of the optima…
parameter estimation