Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify
probability density or mass functions up to an unknown normalizing constant. Unlike most …

Abstract Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can
be interpreted as stochastic neural networks. They have attracted much attention as building …

The restricted Boltzmann machine (RBM) is a two-layered network of stochastic units with
undirected connections between pairs of units in the two layers. The two layers of nodes are …

We propose a few remedies to improve training of Gaussian-Bernoulli restricted Boltzmann
machines (GBRBM), which is known to be difficult. Firstly, we use a different …

Restricted Boltzmann Machine (RBM) is an energy-based, undirected graphical model. It is
commonly used for unsupervised and supervised machine learning. Typically, RBM is …

In this paper, we study a model that we call Gaussian-Bernoulli deep Boltzmann machine
(GDBM) and discuss potential improvements in training the model. GDBM is designed to be …

Energy-based models (EBMs) are generative models inspired by statistical physics with a
wide range of applications in unsupervised learning. Their performance is well measured by …

Generative adversarial networks (GANs) have been impactful on many problems and
applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the …

Boltzmann machines are often used as building blocks in greedy learning of deep networks.
However, training even a simplified model, known as restricted Boltzmann machine (RBM) …

We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines
(GRBMs) from the perspective of density models. The key aspect of this analysis is to show …