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Enhanced Gradient for Training Restricted Boltzmann Machines

https://doi.org/10.1162/neco_a_00397
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-15825-4_26openalex-snapshot
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines
inferredcites →https://doi.org/10.1007/978-3-642-21735-7_2openalex-snapshot
Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines
inferredcites →https://doi.org/10.1007/978-3-642-35289-8_32openalex-snapshot
A Practical Guide to Training Restricted Boltzmann Machines
inferredcites →https://doi.org/10.1007/bf00341287openalex-snapshot
Parametric Inference for imperfectly observed Gibbsian fields
inferredcites →https://doi.org/10.1016/j.knosys.2014.03.016openalex-snapshot
Enhancing performance of restricted Boltzmann machines via log-sum regularization
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot
Deep learning in neural networks: An overview
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.004openalex-snapshot
Measuring the usefulness of hidden units in Boltzmann machines with mutual information
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cvpr.2007.383157openalex-snapshot
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
inferredcites →https://doi.org/10.1109/ijcnn.2010.5596837openalex-snapshot
Parallel tempering is efficient for learning restricted Boltzmann machines
inferredcites →https://doi.org/10.1109/ijcnn.2013.6706831openalex-snapshot
Gaussian-Bernoulli deep Boltzmann machine
inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1145/1390156.1390290openalex-snapshot
Training restricted Boltzmann machines using approximations to the likelihood gradient
inferredcites →https://doi.org/10.1145/1553374.1553453openalex-snapshot
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
inferredcites →https://doi.org/10.1162/089976602760128018openalex-snapshot
Training Products of Experts by Minimizing Contrastive Divergence
inferredcites →https://doi.org/10.1162/neco.2008.11-07-647openalex-snapshot
Justifying and Generalizing Contrastive Divergence
inferredcites →https://doi.org/10.1561/2200000006openalex-snapshot
Learning Deep Architectures for AI
inferredcites →https://doi.org/10.1561/9781601982957openalex-snapshot
Learning Deep Architectures for AI
inferredcites →https://doi.org/10.48550/arxiv.1012.3476openalex-snapshot
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs
inferredcites →https://doi.org/10.7551/mitpress/7503.001.0001openalex-snapshot
Advances in Neural Information Processing Systems 19

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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How to Pretrain Deep Boltzmann Machines in Two Stages
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Evolution Strategies with an RBM-Based Meta-Model
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A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines
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