Temperature based Restricted Boltzmann Machines
Temperature based Restricted Boltzmann Machines is a scholarly work by Changyun Wen, published in 2016 in ''Scientific Reports''. The main subjects of the publication include deep belief network, work, set, biological function, Boltzmann constant, Boltzmann machine, artificial neural network, lock, generative adversarial network, point, Boltzmann distribution, deep learning, graphical model, physics-informed neural networks, machine learning, computer science, statistical physics, restricted Boltzmann machine, and artificial intelligence. The authors propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers.