Aug 4, 2011 · Then to emphasize that SGRBMs can learn more discriminative features we applied SGRBMs to pretrain deep networks for classification tasks.
Abstract. Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers.
Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, theresulting states of ...
Mar 8, 2023 · Sparse Group Restricted Boltzmann Machines ; Authors. Heng Luo. Shanghai Jiao Tong University. Ruimin Shen. Shanghai Jiao Tong University.
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Aug 30, 2010 · The proposed sparse group RBMs are applied to three tasks: modeling patches of natural images, modeling handwritten digits and pretaining a deep ...
A Restricted Boltzmann Machine (RBM) is a genera- tive artificial neural network that learns the probability distribution of its inputs through a series of.
In this paper, we study a new regularization term for sparse hidden units activation in the context of Restricted Boltzmann Machine (RBM). Our proposition is ...
When adapted to the MNIST data set, a two-layer sparse group Boltzmann machine achieves an error rate of 0.84%, which is, to our knowledge, the best published ...
A New Sparse Restricted Boltzmann Machine - World Scientific Publishing
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Simulations show that compared with SRBM, ESRBM has smaller reconstruction error and lower computational complexity, and that for supervised learning ...
Restricted Boltzmann Machines (RBMs) are a common family of undirected graphical models with latent variables. An RBM is described by a bipartite graph, with ...
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