Summary of Cormf: Criticality-ordered Recurrent Mean Field Ising Solver, by Zhenyu Pan et al.
CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver
by Zhenyu Pan, Ammar Gilani, En-Jui Kuo, Zhuo Liu
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Criticality-ordered Recurrent Mean Field (CoRMF) is an RNN-based efficient Ising model solver for forward Ising problems. It leverages a criticality-ordered spin sequence, generated by a greedy algorithm, to enable the unification of variational mean-field and RNNs. This allows for efficient probabilistic inference on generally intractable Ising models. The method is well-modulized, model-independent, and expressive, making it applicable to any forward Ising inference problems with minimal effort. Computationally, CoRMF solves the Ising problems using a variance-reduced Monte Carlo gradient estimator, allowing for self-training without data or evidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoRMF is a new way to solve complex problems in physics called Ising models. It uses special kinds of neural networks (RNNs) to find the best solution. The method works by first sorting important parts of the problem and then using the RNNs to find the answer. This approach is very efficient and can be used for many different types of problems. |
Keywords
* Artificial intelligence * Inference * Rnn * Self training
