Search Results for author: Hiroshi Yamakawa

Found 8 papers, 1 papers with code

Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

no code implementations15 Mar 2021 Tadahiro Taniguchi, Hiroshi Yamakawa, Takayuki Nagai, Kenji Doya, Masamichi Sakagami, Masahiro Suzuki, Tomoaki Nakamura, Akira Taniguchi

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence, is one of the goals in artificial intelligence and developmental robotics.

Hippocampal formation-inspired probabilistic generative model

no code implementations12 Mar 2021 Akira Taniguchi, Ayako Fukawa, Hiroshi Yamakawa

The HPF-PGM is a computational model that is highly consistent with the anatomical structure and functions of the HPF, in contrast to typical conventional SLAM models.

Hippocampus Simultaneous Localization and Mapping

The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain

no code implementations6 Mar 2021 Hiroshi Yamakawa

Brain-inspired AGI development, in other words, cutting down the design space to look more like a biological brain, which is an existing model of a general intelligence, is a promising plan for solving this problem.


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.

Representation Learning

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.

General Reinforcement Learning

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.

Representation Learning

Bayesian Inference of Self-intention Attributed by Observer

no code implementations12 Oct 2018 Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Tatsuji Takahashi, Michita Imai

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging.

Bayesian Inference

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