5 citations · 5 across the 10 of their papers we have counts for
10 papers
Approximate reservoir computing with a semiconductor laser for reducing energy consumption
Tatsuki Ito, Kazutaka Kanno, Satoshi Kawakami +1
Photonic reservoir computing is a promising physical machine-learning approach for time-series prediction. Quantization of the reservoir response signal is an important considerati…
Photonic reservoir computing with complex networks
Sion Park, Kohei Watabe, Satoshi Sunada +2
Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, br…
Adaptive Sensing of Continuous Physical Systems for Machine Learning
Felix Köster, Atsushi Uchida
Physical dynamical systems can be viewed as natural information processors: their systems preserve, transform, and disperse input information. This perspective motivates learning n…
Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems
Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana +2
Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optic…
Photonic decision making using optical frequency difference detection in mutually-coupled semiconductor lasers
Hidetoshi Taira, Takatomo Mihana, Shun Kotoku +4
As electronic computing approaches its performance limits, photonic accelerators have emerged as promising alternatives. Photonic accelerators exploiting semiconductor-laser synchr…
Quantum spatial best-arm identification via quantum walks
Tomoki Yamagami, Etsuo Segawa, Takatomo Mihana +3
Quantum reinforcement learning has emerged as a framework combining quantum computation with sequential decision-making, and applications to the multi-armed bandit (MAB) problem ha…
Attention-Enhanced Reservoir Computing as a Multiple Dynamical System Approximator
Felix Köster, Kazutaka Kanno, Atsushi Uchida
Reservoir computing has proven effective for tasks such as time-series prediction, particularly in the context of chaotic systems. However, conventional reservoir computing framewo…
Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks
Satoshi Sunada, Tomoaki Niiyama, Kazutaka Kanno +4
The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical n…
Experimental control of mode-competition dynamics in a chaotic multimode semiconductor laser for decision making
Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno +2
Photonic computing has been widely used to accelerate the computational performance in machine learning. Photonic decision-making is a promising approach that uses photonic computi…
Attention-Enhanced Reservoir Computing
Felix Köster, Kazutaka Kanno, Jun Ohkubo +1
Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series rema…