用扩散桥模型无偏估计互信息，解决传统方法难处理的数据问题。
InfoBridge: Mutual Information estimation via Bridge Matching
- 将互信息估计转化为域迁移问题，利用扩散桥建模。
- 在低维、图像和高互信息数据上均表现优于传统方法。
- 适合需要精确互信息估计的科研与生物序列分析场景。
扩散桥模型在生成建模中表现出强大能力。本文利用其优势解决机器学习与信息论中的另一关键问题——两随机变量间互信息（MI）的估计。通过将MI估计巧妙地构造成域迁移问题，我们构建了一个对困难数据具有鲁棒性的无偏估计器。在三个标准互信息估计基准测试中——低维、基于图像和高互信息数据——以及真实世界数据（蛋白质语言模型嵌入）上，验证了该估计器的优越性能。
原文摘要 · Abstract (English)
Diffusion bridge models have recently become a powerful tool in the field of generative modeling. In this work, we leverage their power to address another important problem in machine learning and information theory, the estimation of the mutual information (MI) between two random variables. Neatly framing MI estimation as a domain transfer problem, we construct an unbiased estimator for data posing difficulties for conventional MI estimators. We showcase the performance of our estimator on three standard MI estimation benchmarks, i.e., low-dimensional, image-based and high MI, and on real-world data, i.e., protein language model embeddings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。