Photonic computing devices are a compelling alternative to conventional computing setups for machine learning applications, as they are nonlinear, fast and easy to parallelize. Recent work demonstrates the potential of these optical systems to process and classify human motion from video.
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References
Antonik, P., Marsal, N., Brunner, D. & Rontani, D. Nat. Mach. Intell. https://doi.org/10.1038/s42256-019-0110-8 (2019).
Jaeger, H. The “Echo State” Approach to Analysing and Training Recurrent Neural Networks (GMD, 2001).
Maass, W., Natschläger, T. & Markram, H. Neural Comp. 14, 2531–2560 (2002).
Fernando, C. & Sojakka, S. in Advances in Artificial Life 588–597 (Springer, 2003).
Dockendorf, K. P., Park, I., He, P., Príncipe, J. C. & DeMarse, T. B. Biosystems 95, 90–97 (2009).
Bueno, J. et al. Optica 5, 756–760 (2018).
Van der Sande, G., Brunner, D. & Soriano, M. C. Nanophotonics 6, 561 (2017).
Shastri, B. J. et al. in Encyclopedia of Complexity and Systems Science (ed. Meyers, R. A.) 702 (Springer, 2018).
Shen, Y. et al. Nat. Photon. 11, 441–446 (2017).
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Lüdge, K., Röhm, A. Computing with a camera. Nat Mach Intell 1, 551–552 (2019). https://doi.org/10.1038/s42256-019-0124-2
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DOI: https://doi.org/10.1038/s42256-019-0124-2