mlsanityData to 14 Oct 2026

Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning

arXiv 2211.16038 ↗First posted 29 Nov 2022cs.LG cs.ET cs.NEcited by 1

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Abstract (arXiv)

We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data. Our approach uses <10% of experimental data needed for best performance and outperforms analytical models for a Mach-Zehnder interferometer mesh.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 1 of its 8 references that are in the map. First posted on arXiv 29 Nov 2022.

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References · 1

  1. 2020Photonics for artificial intelligence and neuromorphic computingcited by 112

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learningcited by 0