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• W4304587443 abstract "Modern machine-learning applications require huge artificial networks demanding computational power and memory. Light-based platforms promise ultrafast and energy-efficient hardware, which may help realize next-generation data processing devices. However, current photonic networks are limited by the number of input-output nodes that can be processed in a single shot. This restricted network capacity prevents their application to relevant large-scale problems such as natural language processing. Here, we realize a photonic processor for supervised learning with a capacity exceeding <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline id=m1> <mml:mrow> <mml:mn>1.5</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> optical nodes, more than one order of magnitude larger than any previous implementation, which enables photonic large-scale text encoding and classification. By exploiting the full three-dimensional structure of the optical field propagating in free space, we overcome the interpolation threshold and reach the over-parameterized region of machine learning, a condition that allows high-performance sentiment analysis with a minimal fraction of training points. Our results provide a novel solution to scale up light-driven computing and open the route to photonic natural language processing." @default.
• W4304587443 created "2022-10-12" @default.
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• W4304587443 date "2022-11-28" @default.
• W4304587443 modified "2023-09-30" @default.
• W4304587443 title "Large-scale photonic natural language processing" @default.
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• W4304587443 doi "https://doi.org/10.1364/prj.472932" @default.
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