Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine
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Summary
It is shown that the TRBM is a universal approximator for Markov chains and generalize the theorem to sequences with longer time dependence, and it is proved that the RTRBM isA universal approxIMator for stochastic processes with _nite time dependence.
- Type
- article
- Published
- 2016-01-01
- Cited by
- 6
- References
- 15
- OpenAlex
- https://openalex.org/W2529583158
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14907010
Keywords
Boltzmann machine, Restricted Boltzmann machine, Boltzmann constant, Block (permutation group theory), Computer science
References
- Unsupervised Learning of Distributions of Binary Vectors Using 2-Layer Networks
- Deep, Narrow Sigmoid Belief Networks Are Universal Approximators
- Synchronous Boltzmann machines can be universal approximators
- Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
- Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines
- Deep Belief Networks Are Compact Universal Approximators
- Training Products of Experts by Minimizing Contrastive Divergence
- The Recurrent Temporal Restricted Boltzmann Machine
- A Fast Learning Algorithm for Deep Belief Nets
- Learning Multilevel Distributed Representations for High-Dimensional Sequences
- Unsupervised learning of distributions on binary vectors using two layer networks
- Geometry and expressive power of conditional restricted Boltzmann machines
- Modeling Human Motion Using Binary Latent Variables
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- Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition
- Face segmentation based on level set and improved DBM prior shape
- Facial emotion recognition via stationary wavelet entropy and Biogeography-based optimization
- Temporal Generative Models for Learning Heterogeneous Group Dynamics of Ecological Momentary Data
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