# Gradient Flows for L2 Support Vector Machine Training

arXiv: https://arxiv.org/abs/2208.04365 · HTML: https://mlsanity.com/paper/2208.04365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [On Exact Computation with an Infinitely Wide Neural Net](https://mlsanity.com/paper/1904.11955/) (2019)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [On the Equivalence between Neural Network and Support Vector Machine](https://mlsanity.com/paper/2111.06063/) (2021)
- [Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning](https://mlsanity.com/paper/2204.11133/) (2022)
- [Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers](https://mlsanity.com/paper/1706.05928/) (2017)

## Abstract (arXiv)

We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations. We thus assume a continuous time perspective on a machine learning problem which may be of interest for implementations on (re)emerging hardware platforms such as analog- or quantum computers.