# Electron-nucleus cross sections from transfer learning

arXiv: https://arxiv.org/abs/2408.09936 · HTML: https://mlsanity.com/paper/2408.09936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)

## Built on by (most cited first, 2 of 2)

- [Generative adversarial neural networks for simulating neutrino interactions](https://mlsanity.com/paper/2502.20244/) (2025)
- [Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data](https://mlsanity.com/paper/2508.00996/) (2025)

## Abstract (arXiv)

Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets ranging from helium-3 to iron.