Structural health monitoring (SHM) is a multi-discipline field that involves the automatic
sensing of structural loads and response by means of a large number of sensors and …

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. The increase in computational power and the …

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

As an important category of deep models, deep generative model has attracted more and
more attention with the proposal of Deep Belief Networks (DBNs) and the fast greedy …

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 …

This paper proposes an identification framework based on a restricted Boltzmann machine
(RBM) for crack identification and extraction from images containing cracks and complicated …

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 …

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) …

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