Constructing Hierarchical Bayesian Networks With Pooling
DOI:
https://doi.org/10.1609/aaai.v32i1.12191Keywords:
Machine Learning, Pattern Recognition, Bayesian Network, Bayesian Brain HypothesisAbstract
Inspired by the Bayesian brain hypothesis and deep learning, we develop a Bayesian autoencoder, a method of constructing recognition systems using a Bayesian network. We construct hierarchical Bayesian networks based on feature extraction and implement pooling to achieve invariance within a Bayesian network framework. The constructed networks propagate information bidirectionally between layers. We expect they will be able to achieve brain-like recognition using local features and global information such as their environments.
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Published
2018-04-29
How to Cite
Nishino, K., & Inaba, M. (2018). Constructing Hierarchical Bayesian Networks With Pooling. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12191
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Student Abstract Track