Constructing Hierarchical Bayesian Networks with Pooling

AAAI 2018 pp. 8125-8126

doi:10.1609/AAAI.V32I1.12191 /aaai/2018/nishino2018aaai-constructing/

Abstract

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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Cite

Text

Nishino and Inaba. "Constructing Hierarchical Bayesian Networks with Pooling." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12191

Markdown

[Nishino and Inaba. "Constructing Hierarchical Bayesian Networks with Pooling." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/nishino2018aaai-constructing/) doi:10.1609/AAAI.V32I1.12191

BibTeX

@inproceedings{nishino2018aaai-constructing,
  title     = {{Constructing Hierarchical Bayesian Networks with Pooling}},
  author    = {Nishino, Kaneharu and Inaba, Mary},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8125-8126},
  doi       = {10.1609/AAAI.V32I1.12191},
  url       = {https://mlanthology.org/aaai/2018/nishino2018aaai-constructing/}
}