Search Results for author: Kosuke Miyoshi

Found 3 papers, 1 papers with code

FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE

no code implementations ICLR 2019 Masanori Yamada, Kim Heecheol, Kosuke Miyoshi, Hiroshi Yamakawa

Previous works succeed in disentangling static factors and dynamic factors by explicitly modeling the priors of latent variables to distinguish between static and dynamic factors.

Disentanglement

Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder

no code implementations22 Mar 2019 Heecheol Kim, Masanori Yamada, Kosuke Miyoshi, Hiroshi Yamakawa

Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.

Disentanglement General Reinforcement Learning +2

FAVAE: Sequence Disentanglement using Information Bottleneck Principle

1 code implementation22 Feb 2019 Masanori Yamada, Heecheol Kim, Kosuke Miyoshi, Hiroshi Yamakawa

Previous models disentangle static and dynamic factors by explicitly modeling the priors of latent variables to distinguish between these factors.

Disentanglement

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