用向量拷贝方法提升互信息估计的精度与泛化能力
Neural Mutual Information Estimation with Vector Copulas
- 基于向量拷贝理论，在复杂度与表达力间找平衡
- 在合成数据和多模态真实数据上表现优于现有方法
- 适合需要高精度互信息估计的研究者使用
互信息（MI）估计是数据科学和机器学习中的基础任务。现有方法主要依赖高度灵活的模型（如神经网络），需大量数据；或过度简化的模型（如高斯拷贝），无法捕捉复杂分布。本文基于最新的向量拷贝理论，提出一种在两者之间进行合理插值的系统性方法，实现复杂度与容量之间的更好权衡。在最先进的合成基准和具有多样化模态的真实数据上的实验表明，所提估计器具有明显优势。
原文摘要 · Abstract (English)
Estimating mutual information (MI) is a fundamental task in data science and machine learning. Existing estimators mainly rely on either highly flexible models (e.g., neural networks), which require large amounts of data, or overly simplified models (e.g., Gaussian copula), which fail to capture complex distributions. Drawing upon recent vector copula theory, we propose a principled interpolation between these two extremes to achieve a better trade-off between complexity and capacity. Experiments on state-of-the-art synthetic benchmarks and real-world data with diverse modalities demonstrate the advantages of the proposed estimator.
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。