Deep Boltzmann

Deep Boltzmann Machines (DBMs) are multi-layered probabilistic models aiming to efficiently learn complex data representations. Current research focuses on improving training efficiency through novel algorithms like unbiased contrastive divergence with optimized initialization, and exploring restricted architectures such as monotone DBMs to enable efficient approximate inference. Furthermore, research investigates the use of specialized hardware, including quantum annealers and Ising machines, to accelerate training and overcome limitations of traditional computing approaches, demonstrating potential for improved data efficiency and generative capabilities in applications like reinforcement learning and image processing.