Search Results for author: Hiroshi Yamakawa

Found 10 papers, 1 papers with code

Recognition of All Categories of Entities by AI

no code implementations13 Aug 2022 Hiroshi Yamakawa, Yutaka Matsuo

Human-level AI will have significant impacts on human society.


Brain-inspired probabilistic generative model for double articulation analysis of spoken language

no code implementations6 Jul 2022 Akira Taniguchi, Maoko Muro, Hiroshi Yamakawa, Tadahiro Taniguchi

This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys.


A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots

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

This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs.

Hippocampal formation-inspired probabilistic generative model

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

In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space.

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.


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 +1

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.


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 reinforcement-learning

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