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Deep neural networks with time-domain synthetic photonic lattices. / Pankov, Artem V.; Sidelnikov, Oleg S.; Vatnik, Ilya D. et al.

European Quantum Electronics Conference, EQEC 2021. The Optical Society, 2021. jsiv_p_3 (Optics InfoBase Conference Papers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Pankov, AV, Sidelnikov, OS, Vatnik, ID, Churkin, DV & Sukhorukov, AA 2021, Deep neural networks with time-domain synthetic photonic lattices. in European Quantum Electronics Conference, EQEC 2021., jsiv_p_3, Optics InfoBase Conference Papers, The Optical Society, 2021 European Quantum Electronics Conference, EQEC 2021 - Part of 2021 Conference on Lasers and Electro-Optics Europe, CLEO 2021, Virtual, Online, Germany, 21.06.2021.

APA

Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Churkin, D. V., & Sukhorukov, A. A. (2021). Deep neural networks with time-domain synthetic photonic lattices. In European Quantum Electronics Conference, EQEC 2021 [jsiv_p_3] (Optics InfoBase Conference Papers). The Optical Society.

Vancouver

Pankov AV, Sidelnikov OS, Vatnik ID, Churkin DV, Sukhorukov AA. Deep neural networks with time-domain synthetic photonic lattices. In European Quantum Electronics Conference, EQEC 2021. The Optical Society. 2021. jsiv_p_3. (Optics InfoBase Conference Papers).

Author

Pankov, Artem V. ; Sidelnikov, Oleg S. ; Vatnik, Ilya D. et al. / Deep neural networks with time-domain synthetic photonic lattices. European Quantum Electronics Conference, EQEC 2021. The Optical Society, 2021. (Optics InfoBase Conference Papers).

BibTeX

@inproceedings{f7559c7e30c04423bd6716d70a06dc43,
title = "Deep neural networks with time-domain synthetic photonic lattices",
abstract = "We reveal that synthetic photonic lattice based on coupled fiber rings can realise deep neural networks foroptical pulse trains, and demonstrate the capabilities in efficient training for signal distortion compensation and nonlinear transformations.",
author = "Pankov, {Artem V.} and Sidelnikov, {Oleg S.} and Vatnik, {Ilya D.} and Churkin, {Dmitry V.} and Sukhorukov, {Andrey A.}",
note = "Funding Information: This work is supported by Ministry of Education and Science of the Russian Federation (FSUS-2020-0034) and the Australian Research Council (DP190100277).; 2021 European Quantum Electronics Conference, EQEC 2021 - Part of 2021 Conference on Lasers and Electro-Optics Europe, CLEO 2021 ; Conference date: 21-06-2021 Through 25-06-2021",
year = "2021",
language = "English",
series = "Optics InfoBase Conference Papers",
publisher = "The Optical Society",
booktitle = "European Quantum Electronics Conference, EQEC 2021",
address = "United States",

}

RIS

TY - GEN

T1 - Deep neural networks with time-domain synthetic photonic lattices

AU - Pankov, Artem V.

AU - Sidelnikov, Oleg S.

AU - Vatnik, Ilya D.

AU - Churkin, Dmitry V.

AU - Sukhorukov, Andrey A.

N1 - Funding Information: This work is supported by Ministry of Education and Science of the Russian Federation (FSUS-2020-0034) and the Australian Research Council (DP190100277).

PY - 2021

Y1 - 2021

N2 - We reveal that synthetic photonic lattice based on coupled fiber rings can realise deep neural networks foroptical pulse trains, and demonstrate the capabilities in efficient training for signal distortion compensation and nonlinear transformations.

AB - We reveal that synthetic photonic lattice based on coupled fiber rings can realise deep neural networks foroptical pulse trains, and demonstrate the capabilities in efficient training for signal distortion compensation and nonlinear transformations.

UR - http://www.scopus.com/inward/record.url?scp=85119976314&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85119976314

T3 - Optics InfoBase Conference Papers

BT - European Quantum Electronics Conference, EQEC 2021

PB - The Optical Society

T2 - 2021 European Quantum Electronics Conference, EQEC 2021 - Part of 2021 Conference on Lasers and Electro-Optics Europe, CLEO 2021

Y2 - 21 June 2021 through 25 June 2021

ER -

ID: 34839308