Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines
Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines is a scholarly work, published in 2018 in ''Neural Processing Letters''. The main subjects of the publication include divergence, Boltzmann machine, mathematics, gradient descent, artificial intelligence, computer science, generative adversarial network, physics-informed neural networks, mathematical optimization, biological function, maximum a posteriori estimation, Boltzmann distribution, energy, machine learning, restricted Boltzmann machine, and algorithm. The paper introduces a new approach to maximum likelihood learning of the parameters of a restricted Boltzmann machine (RBM).