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New research explores neural network and transfer learning for Ising models · 3 sources tracked

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore the application of neural networks and transfer learning to high-dimensional Ising models. The first paper investigates out-of-distribution performance of various neural architectures, finding that Transformer-based models and convolutional neural networks employ different statistical strategies that can lead to apparent robustness without true physical rule learning. The second paper introduces Trans-Ising, a transfer learning method designed to improve Ising model estimation by effectively utilizing auxiliary datasets, demonstrating lower estimation errors compared to target-only methods. AI

IMPACT These studies highlight potential pitfalls in applying neural networks to scientific discovery and propose methods to improve model performance using transfer learning.

RANK_REASON Two new arXiv papers on machine learning applications to statistical physics models.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research explores neural network and transfer learning for Ising models · 3 sources tracked

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Yuan-Bin Zhu, Shuang Qiao, Shi-Ju Ran ·

    Out-of-distribution Neural Inference in Dynamical Ising Models

    arXiv:2607.03039v1 Announce Type: new Abstract: Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects transferable physical rule learning. We address this…

  2. arXiv stat.ML TIER_1 English(EN) · Joonho Kim, Seyoung Park ·

    Transfer Learning in High-dimensional Ising Models

    arXiv:2607.03005v1 Announce Type: cross Abstract: In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging. To address this, we propose Trans-Ising, a transfer learn…

  3. arXiv stat.ML TIER_1 English(EN) · Seyoung Park ·

    Transfer Learning in High-dimensional Ising Models

    In high-dimensional Ising model estimation, target sample sizes are often limited, and effectively using auxiliary binary datasets of unknown relevance remains challenging. To address this, we propose Trans-Ising, a transfer learning method that combines a loss-based source scree…

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