[HTML][HTML] Recent advances in physical reservoir computing: A review
Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …
processing. It is derived from several recurrent neural network models, including echo state …
Fundamental physics and applications of skyrmions: A review
Beyond-CMOS computational paradigms are necessary to solving the problems that we face
with modern computers in achieving scalability, low energy consumption, reduced latency …
with modern computers in achieving scalability, low energy consumption, reduced latency …
Experimental photonic quantum memristor
Memristive devices are a class of physical systems with history-dependent dynamics
characterized by signature hysteresis loops in their input–output relations. In the past few …
characterized by signature hysteresis loops in their input–output relations. In the past few …
Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation
We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network
(ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short …
(ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short …
Emerging opportunities and challenges for the future of reservoir computing
Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …
Polaritonic neuromorphic computing outperforms linear classifiers
Machine learning software applications are ubiquitous in many fields of science and society
for their outstanding capability to solve computationally vast problems like the recognition of …
for their outstanding capability to solve computationally vast problems like the recognition of …
A survey on reservoir computing and its interdisciplinary applications beyond traditional machine learning
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …
network in which neurons are randomly connected. Once initialized, the connection …
A survey of approaches for implementing optical neural networks
Conventional neural networks are software simulations of artificial neural networks (ANNs)
implemented on von Neumann machines. This technology has recently encountered …
implemented on von Neumann machines. This technology has recently encountered …
Large-scale quantum reservoir learning with an analog quantum computer
Quantum machine learning has gained considerable attention as quantum technology
advances, presenting a promising approach for efficiently learning complex data patterns …
advances, presenting a promising approach for efficiently learning complex data patterns …
Echo state networks-based reservoir computing for mnist handwritten digits recognition
N Schaetti, M Salomon… - 2016 IEEE Intl conference …, 2016 - ieeexplore.ieee.org
Reservoir Computing is an attractive paradigm of recurrent neural network architecture, due
to the ease of training and existing neuromorphic implementations. Successively applied on …
to the ease of training and existing neuromorphic implementations. Successively applied on …