The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are two kinds of hidden units, namely, gate units and subspace units. The subspace units reflect variations of a pattern in data and the gate unit is responsible for activating the subspace units. Additionally, the gate unit can be seen as a pooling feature. We evaluate the behavior of subspaceRBM through experiments with MNIST digit recognition task, measuring reconstruction error and classification error
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representi...
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representi...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...
The restricted Boltzmann machine (RBM) is a two-layered network of stochastic units with undirected ...
We describe discrete restricted Boltzmann machines: probabilistic graphical mod-els with bipartite i...
International audienceExtracting automatically the complex set of features composing real high-dimen...
The use of Restricted Boltzmann Machines (RBM) is proposed in this paper as a non-linear transformat...
Restricted Boltzmann Machine (RBM) has been applied to a wide variety of tasks due to its advantage ...
For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
A restricted Boltzmann machine (RBM) learns a probability distribution over its input samples and ha...
We present a Classification Restricted Boltzmann Machine (ClassRBM) hardware for embedded machines wi...
Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connection...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representi...
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representi...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...
The restricted Boltzmann machine (RBM) is a two-layered network of stochastic units with undirected ...
We describe discrete restricted Boltzmann machines: probabilistic graphical mod-els with bipartite i...
International audienceExtracting automatically the complex set of features composing real high-dimen...
The use of Restricted Boltzmann Machines (RBM) is proposed in this paper as a non-linear transformat...
Restricted Boltzmann Machine (RBM) has been applied to a wide variety of tasks due to its advantage ...
For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
A restricted Boltzmann machine (RBM) learns a probability distribution over its input samples and ha...
We present a Classification Restricted Boltzmann Machine (ClassRBM) hardware for embedded machines wi...
Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connection...
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipartite in...
International audienceThis review deals with Restricted Boltzmann Machine (RBM) under the light of s...
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representi...
Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representi...
Restricted Boltzmann machines are a generative neural network. They summarize their input data to bu...