56 citations · 81 across the 16 of their papers we have counts for
16 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 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…
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…
Chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection
Ryugo Iwami, Kazutaka Kanno, Atsushi Uchida
Photonic computing is attracting increasing interest to accelerate information processing in machine learning applications. The mode-competition dynamics of multimode semiconductor…
Parallel photonic accelerator for decision making using optical spatiotemporal chaos
Kensei Morijiri, Kento Takehana, Takatomo Mihana +3
Photonic accelerators have attracted increasing attention in artificial intelligence applications. The multi-armed bandit problem is a fundamental problem of decision making using…
Analysis of temporal structure of laser chaos by Allan variance
Naoki Asuke, Nicolas Chauvet, André Röhm +6
Allan variance has been widely utilized in evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long.…
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…
Generative adversarial network based on chaotic time series
Makoto Naruse, Takashi Matsubara, Nicolas Chauvet +3
Generative adversarial network (GAN) is gaining increased importance in artificially constructing natural images and related functionalities wherein two networks called generator a…