# Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning

arXiv: https://arxiv.org/abs/2211.16038 · HTML: https://mlsanity.com/paper/2211.16038/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 1 of 1)

- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)

## Built on by (most cited first, 1 of 1)

- [Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning](https://mlsanity.com/paper/2308.11630/) (2023)

## 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.