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English(EN) Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning

新的 arXiv 论文将物理学概念与机器学习应用联系起来

一篇新发表在 arXiv 上的综述论文探讨了控制论、最优传输、概率推理、非平衡热力学与机器学习之间的深刻联系。该论文强调了这些不同领域如何围绕在各种约束条件下优化自由能类泛函这一共同主题。它旨在通过对这些概念进行导览,即使没有物理学专业知识也能理解,并展示了在强化学习、变分推理和生成模型等领域的应用。 AI

影响 该论文将理论物理学概念与机器学习应用联系起来,有可能为先进的 AI 开发提供新的框架。

排序理由 该条目是一篇发表在 arXiv 上的学术论文,详细介绍了不同科学领域之间的理论联系。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的 arXiv 论文将物理学概念与机器学习应用联系起来

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该条目是一篇发表在 arXiv 上的学术论文,详细介绍了不同科学领域之间的理论联系。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Emmy Blumenthal, Nikolas Claussen, Benjamin Eysenbach, Catherine Ji, Gautam Reddy, Colin Scheibner, Benjamin Sorkin ·

    融合控制、推理、传输与热力学:从理论到学习中的应用

    arXiv:2609.15897v1 Announce Type: cross Abstract: The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to the foreground fundamental connections between subdisciplines of physics, appli…