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Implementation of Explicit Knowledge on Deep Learning

Takashi Matsubara

Since deep learning is a very flexible framework, it works well for various tasks without expert knowledge, but it also has difficulty leveraging explicit knowledge. Deep learning always requires massive dataset and is applicable to limited tasks. I introduce deep generative model, which is a Bayesian network implemented on deep neural networks. By expressing our knowledge as the network structure, deep generative model works for a small-sized dataset and provides interpretable results.

07.09.2018 - 10:15
B 017