The replacement of symmetric connections in this system with directed connections that form an acyclic graph is used to create a sigmoid belief network, which eliminates this system's requirement of a negative phase.
The replacement of symmetric connections in this system with directed connections that form an acyclic graph is used to create a sigmoid belief network, which eliminates this system's requirement of a negative phase. This model employs symmetrically connected stochastic components which are partitioned into visible and hidden layers, with the visible layer clamped to specific environment-determined states. The goal of this type of neural network is to maximize a likelihood function, which it does by adjusting synaptic weights in proportion to the correlation between the states of its component neurons. Learning in these networks is accomplished via Gibbs sampling combined with a downward adjustment of the network "temperature," in a process called simulated annealing. For 10 points, identify this type of stochastic neural network which takes its name from the distribution that governs its dynamics at thermal equilibrium.
The replacement of symmetric connections in this system with directed connections that form an acyclic graph is used to create a sigmoid belief network, which eliminates this system's requirement of a negative phase.
Boltzmann machine [prompt on "neural network"]