Search Results for author: Atsushi Uchida

Found 11 papers, 0 papers with code

Attention-Enhanced Reservoir Computing

no code implementations27 Dec 2023 Felix Köster, Kazutaka Kanno, Jun Ohkubo, Atsushi Uchida

Photonic reservoir computing has been recently utilized in time series forecasting as the need for hardware implementations to accelerate these predictions has increased.

Temporal Sequences Time Series +1

Ultrafast single-channel machine vision based on neuro-inspired photonic computing

no code implementations15 Feb 2023 Tomoya Yamaguchi, Kohei Arai, Tomoaki Niiyama, Atsushi Uchida, Satoshi Sunada

This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain.

Anomaly Detection

Parallel photonic accelerator for decision making using optical spatiotemporal chaos

no code implementations12 Oct 2022 Kensei Morijiri, Kento Takehana, Takatomo Mihana, Kazutaka Kanno, Makoto Naruse, Atsushi Uchida

We solve a 512-armed bandit problem online, which is much larger than previous experiments by two orders of magnitude.

Decision Making

Controlling chaotic itinerancy in laser dynamics for reinforcement learning

no code implementations12 May 2022 Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno, Satoshi Sunada, Makoto Naruse, Atsushi Uchida

In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning.

BIG-bench Machine Learning reinforcement-learning +1

Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing

no code implementations22 May 2021 Satoshi Sunada, Atsushi Uchida

In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip.

Time Series Prediction

Adaptive model selection in photonic reservoir computing by reinforcement learning

no code implementations27 Apr 2020 Kazutaka Kanno, Makoto Naruse, Atsushi Uchida

Here, we propose a scheme of adaptive model selection in photonic reservoir computing using reinforcement learning.

Load Forecasting Model Selection +4

Lotka-Volterra competition mechanism embedded in a decision-making method

no code implementations29 Jul 2019 Tomoaki Niiyama, Genki Furuhata, Atsushi Uchida, Makoto Naruse, Satoshi Sunada

Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications.

Decision Making

Generative adversarial network based on chaotic time series

no code implementations24 May 2019 Makoto Naruse, Takashi Matsubara, Nicolas Chauvet, Kazutaka Kanno, Tianyu Yang, Atsushi Uchida

Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data.

Generative Adversarial Network Time Series +1

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

no code implementations26 Mar 2018 Makoto Naruse, Takatomo Mihana, Hirokazu Hori, Hayato Saigo, Kazuya Okamura, Mikio Hasegawa, Atsushi Uchida

In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series.

Decision Making reinforcement-learning +3

Ultrafast photonic reinforcement learning based on laser chaos

no code implementations14 Apr 2017 Makoto Naruse, Yuta Terashima, Atsushi Uchida, Song-Ju Kim

Reinforcement learning involves decision making in dynamic and uncertain environments, and constitutes one important element of artificial intelligence (AI).

Decision Making reinforcement-learning +1

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