We propose a Boltzmann machine formulated as a probabilistic model where every random variable takes bounded continuous values, and we derive the Thouless-Anderson-Palmer equation for the model. The proposed model includes the non-negative Boltzmann machine and the Sherrington-Kirkpatrick model with spin-S at S→∞ as a special case. It is known that the Sherrington-Kirkpatrick model with spin-S has a spin glass phase. Thus, the proposed Boltzmann machine is expected to be able to learn practical complex data
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
Some interesting recent advances in the theoretical understanding of neural networks have been infor...
Abstract-The idea of Hopfield network is based on the king spin glass model in which each spin has o...
[[abstract]]The authors introduce a continuous stochastic generative model that can model continuous...
International audienceRestricted Boltzmann machines (RBMs) are energy-based neural networks which ar...
A general Boltzmann machine with continuous visible and discrete integer valued hidden states is int...
We define a new network structure to realize a continuous version of the Boltzmann Machine (BM). Ba...
A mathematical model is presented for the description of parallel Boltzmann machines. The framework ...
In this paper we present a formal model of the Boltzmann machine and a discussion of two different a...
A mathematical model is presented for the description of synchronously parallel Boltzmann machines. ...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...
We describe discrete restricted Boltzmann machines: probabilistic graphical mod-els with bipartite i...
Boltzmann learning underlies an artificial neural network model known as the Boltzmann machine that ...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
Some interesting recent advances in the theoretical understanding of neural networks have been infor...
Abstract-The idea of Hopfield network is based on the king spin glass model in which each spin has o...
[[abstract]]The authors introduce a continuous stochastic generative model that can model continuous...
International audienceRestricted Boltzmann machines (RBMs) are energy-based neural networks which ar...
A general Boltzmann machine with continuous visible and discrete integer valued hidden states is int...
We define a new network structure to realize a continuous version of the Boltzmann Machine (BM). Ba...
A mathematical model is presented for the description of parallel Boltzmann machines. The framework ...
In this paper we present a formal model of the Boltzmann machine and a discussion of two different a...
A mathematical model is presented for the description of synchronously parallel Boltzmann machines. ...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...
We describe discrete restricted Boltzmann machines: probabilistic graphical mod-els with bipartite i...
Boltzmann learning underlies an artificial neural network model known as the Boltzmann machine that ...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
Some interesting recent advances in the theoretical understanding of neural networks have been infor...