Search Results for author:
Found 3 papers, 1 papers with code
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
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.
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.
FAVAE: Sequence Disentanglement using Information Bottleneck Principle
Previous models disentangle static and dynamic factors by explicitly modeling the priors of latent variables to distinguish between these factors.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
