Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Yusuke Sakemi"'
Autor:
Yudai Ebato, Sou Nobukawa, Yusuke Sakemi, Haruhiko Nishimura, Takashi Kanamaru, Nina Sviridova, Kazuyuki Aihara
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract The echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency. I
Externí odkaz:
https://doaj.org/article/d59bb06dcfa64ad7abd39657092772ba
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-11 (2024)
Abstract Reservoir computing (RC) can efficiently process time-series data by mapping the input signal into a high-dimensional space via randomly connected recurrent neural networks (RNNs), which are referred to as a reservoir. The high-dimensional r
Externí odkaz:
https://doaj.org/article/4654710115f44d0a89e300766818d74c
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training schemes, the error backpropagation method that directly uses the fi
Externí odkaz:
https://doaj.org/article/cd170e0c1ca04ba3827fd23195b23269
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
Abstract Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called “reservoirs.” To implem
Externí odkaz:
https://doaj.org/article/739fe033c48f4169b1048236ff7c25e0
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 34:394-408
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de0977b33a691c47088b2c1b283e309d
https://doi.org/10.1109/tnnls.2021.3095068
https://doi.org/10.1109/tnnls.2021.3095068
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 69:3640-3644
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5658ee25197153e78608e98698e3b8e8
https://doi.org/10.1109/tcsii.2022.3184313
https://doi.org/10.1109/tcsii.2022.3184313
Publikováno v:
2022 IEEE International Symposium on Circuits and Systems (ISCAS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d059598fef25f66ca7b4baf8de8f88d
https://doi.org/10.1109/iscas48785.2022.9937662
https://doi.org/10.1109/iscas48785.2022.9937662
Publikováno v:
Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
Scientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c63fa5adb80d9951c6cf1069280ae5e
http://arxiv.org/abs/2006.06218
http://arxiv.org/abs/2006.06218
Publikováno v:
Physical Review A. 96
With 800-nm, 25-fs elliptically polarized ionization pulses, we observe molecular frame photoelectron angular distributions (MF-PADs) correlated with different dissociative ionization channels: ${\mathrm{OCS}}^{+}\ensuremath{\rightarrow}{\mathrm{S}}^
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc9ebabf1f6b0c6aec40b90b39390d59
https://doi.org/10.1103/physreva.96.011401
https://doi.org/10.1103/physreva.96.011401
Publikováno v:
Physical Review A. 90
We present the observations of the phase differences $\ensuremath{\Delta}{\ensuremath{\phi}}_{\mathrm{HH}}^{(2n)}$ between adjacent high-order harmonics generated from Ar and ${\mathrm{N}}_{2}$ at the near-threshold region. The $\ensuremath{\Delta}{\
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::197dc388829a91e7a7775c683d621ce7
https://doi.org/10.1103/physreva.90.063403
https://doi.org/10.1103/physreva.90.063403