3 citations · 4 across the 7 of their papers we have counts for
7 papers
Parallel bandit architecture based on laser chaos for reinforcement learning
Takashi Urushibara, Nicolas Chauvet, Satoshi Kochi +5
Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of m…
Controlling chaotic itinerancy in laser dynamics for reinforcement learning
Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno +3
Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully utilized for achie…
Photonic reinforcement learning based on optoelectronic reservoir computing
Kazutaka Kanno, Atsushi Uchida
Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot contro…
Using multidimensional speckle dynamics for high-speed, large-scale, parallel photonic computing
Satoshi Sunada, Kazutaka Kanno, Atsushi Uchida
The recent rapid increase in demand for data processing has resulted in the need for novel machine learning concepts and hardware. Physical reservoir computing and an extreme learn…
Adaptive model selection in photonic reservoir computing by reinforcement learning
Kazutaka Kanno, Makoto Naruse, Atsushi Uchida
Photonic reservoir computing is an emergent technology toward beyond-Neumann computing. Although photonic reservoir computing provides superior performance in environments whose ch…
Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series
Shungo Takeuchi, Mikio Hasegawa, Kazutaka Kanno +3
Dynamic channel selection is among the most important wireless communication elements in dynamically changing electromagnetic environments wherein a user can experience improved co…