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Found 11 papers, 0 papers with code
Attention-Enhanced Reservoir Computing
Photonic reservoir computing has been recently utilized in time series forecasting as the need for hardware implementations to accelerate these predictions has increased.
Ultrafast single-channel machine vision based on neuro-inspired photonic computing
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.
Parallel photonic accelerator for decision making using optical spatiotemporal chaos
We solve a 512-armed bandit problem online, which is much larger than previous experiments by two orders of magnitude.
Parallel bandit architecture based on laser chaos for reinforcement learning
Q-learning is a well-known approach in reinforcement learning that can deal with many states.
Controlling chaotic itinerancy in laser dynamics for reinforcement learning
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.
Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing
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.
Adaptive model selection in photonic reservoir computing by reinforcement learning
Here, we propose a scheme of adaptive model selection in photonic reservoir computing using reinforcement learning.
Lotka-Volterra competition mechanism embedded in a decision-making method
Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications.
Generative adversarial network based on chaotic time series
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.
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos
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.
Ultrafast photonic reinforcement learning based on laser chaos
Reinforcement learning involves decision making in dynamic and uncertain environments, and constitutes one important element of artificial intelligence (AI).
