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Subspace Restricted Boltzmann Machine
"... The subspace Restricted Boltzmann Machine (subspaceRBM) is a thirdorder 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 ..."
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The subspace Restricted Boltzmann Machine (subspaceRBM) is a thirdorder 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
The prototypical Restricted Boltzmann Machine
"... We introduce the spike and slab Restricted Boltzmann Machine, characterized by having both a realvalued vector, the slab, and a binary variable, the spike, associated with each unit in the hidden layer. The model possesses some practical properties such as being amenable to Block Gibbs sampling as ..."
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as well as being capable of generating similar latent representations of the data to the recently introduced mean and covariance Restricted Boltzmann Machine. We illustrate how the spike and slab Restricted Boltzmann Machine achieves competitive performance on the CIFAR10 object recognition task.
TensorVariate Restricted Boltzmann Machines
"... Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An underexplored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resul ..."
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Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An underexplored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus re
Tensorvariate Restricted Boltzmann Machines
"... Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An underexplored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resul ..."
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Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An underexplored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus re
Sparse Group Restricted Boltzmann Machines
"... Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1=l2 regularization upon the activation probabilit ..."
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Cited by 2 (0 self)
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Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1=l2 regularization upon the activation
Phone Recognition with the MeanCovariance Restricted Boltzmann Machine
"... Straightforward application of Deep Belief Nets (DBNs) to acoustic modeling produces a rich distributed representation of speech data that is useful for recognition and yields impressive results on the speakerindependent TIMIT phone recognition task. However, the firstlayer GaussianBernoulli Rest ..."
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Cited by 64 (11 self)
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Bernoulli Restricted Boltzmann Machine (GRBM) has an important limitation, shared with mixtures of diagonalcovariance Gaussians: GRBMs treat different components of the acoustic input vector as conditionally independent given the hidden state. The meancovariance restricted Boltzmann machine (mcRBM), first introduced
Results 1  10
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