Add to ORKGWe define a new network structure to realize a continuous version of the Boltzmann Machine (BM). Based on Mean Field (MF) theory for continuous and multidimensional elements named "rotors" (Gisln, Peterson, and Sdeberg, 92) we derive the corresponding MF learning algorithm. Simulations demonstrate the learning capability of this network for continuous and piecewise continuous mappings. The rotor neurons are specially suited for cyclic problems of arbitrary dimension
The brain's cognitive power does not arise on exacting digital precision in high-performance computi...
We present a general formulation for a network of stochastic directional units. This formulation is ...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...
We define a new network structure to realize a continuous version of the Boltzmann Machine (BM). Bas...
The learning process in Boltzmann machines is computationally very expensive. The computational comp...
Various applications of the mean field theory (MFT) technique for obtaining solutions close to optim...
Boltzmann learning underlies an artificial neural network model known as the Boltzmann machine that ...
Abstract-The idea of Hopfield network is based on the king spin glass model in which each spin has o...
In this paper we formulate the Expectation Maximization (EM) algorithm for Boltzmann Machines and we...
The computotionol power of massively parallel networks of simple processing elements resides in the ...
[[abstract]]The authors introduce a continuous stochastic generative model that can model continuous...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...
We propose a Boltzmann machine formulated as a probabilistic model where every random variable takes...
A specific type of neural networks, the Restricted Boltzmann Machines (RBM), are implemented for cla...
none4siA specific type of neural network, the Restricted Boltzmann Machine (RBM), is implemented for...
The brain's cognitive power does not arise on exacting digital precision in high-performance computi...
We present a general formulation for a network of stochastic directional units. This formulation is ...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...
We define a new network structure to realize a continuous version of the Boltzmann Machine (BM). Bas...
The learning process in Boltzmann machines is computationally very expensive. The computational comp...
Various applications of the mean field theory (MFT) technique for obtaining solutions close to optim...
Boltzmann learning underlies an artificial neural network model known as the Boltzmann machine that ...
Abstract-The idea of Hopfield network is based on the king spin glass model in which each spin has o...
In this paper we formulate the Expectation Maximization (EM) algorithm for Boltzmann Machines and we...
The computotionol power of massively parallel networks of simple processing elements resides in the ...
[[abstract]]The authors introduce a continuous stochastic generative model that can model continuous...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...
We propose a Boltzmann machine formulated as a probabilistic model where every random variable takes...
A specific type of neural networks, the Restricted Boltzmann Machines (RBM), are implemented for cla...
none4siA specific type of neural network, the Restricted Boltzmann Machine (RBM), is implemented for...
The brain's cognitive power does not arise on exacting digital precision in high-performance computi...
We present a general formulation for a network of stochastic directional units. This formulation is ...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...