# Machine learning the Ising transition: A comparison between discriminative and generative approaches

arXiv: https://arxiv.org/abs/2411.19370 · HTML: https://mlsanity.com/paper/2411.19370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Solving Statistical Mechanics Using Variational Autoregressive Networks](https://mlsanity.com/paper/1809.10606/) (2018)
- [Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence](https://mlsanity.com/paper/2306.02572/) (2023)
- [Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks](https://mlsanity.com/paper/2105.05650/) (2021)
- [Machine learning phase transitions: Connections to the Fisher information](https://mlsanity.com/paper/2311.10710/) (2023)
- [Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion](https://mlsanity.com/paper/2311.09128/) (2023)

## Abstract (arXiv)

The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a discriminative or a generative method. In general, it is unclear which of these two approaches is most suitable for a given problem. The choice is expected to depend on factors such as the availability of system knowledge, dataset size, desired accuracy, computational resources, and other considerations. In this work, we answer the question of how one should approach the solution of phase-classification problems by performing a numerical case study on the thermal phase transition in the classical two-dimensional square-lattice ferromagnetic Ising model.