Modeling high-dimensional dependencies while keeping likelihoods tractable remains challenging. Classical vine-copula pipelines are interpretable but can be expensive, while many neural estimators are flexible but less structured. In this work, we propose Vine Denoising Copula (VDC), an amortized vine-copula pipeline that trains a single bivariate denoising model and reuses it across all vine edges. For each edge, given pseudo-observations, the model predicts a density grid. We then apply an IPFP/Sinkhorn projection that enforces non-negativity, unit mass, and uniform marginals. This keeps the exact vine likelihood and preserves the usual copula interpretation while replacing repeated per-edge optimization with GPU inference. Across synthetic and real-data benchmarks, VDC delivers strong bivariate density accuracy, competitive MI/TC estimation, and substantial speedups for high-dimensional vine fitting. In practice, these gains make explicit information estimation and dependence decomposition feasible at scales where repeated vine fitting would otherwise be costly, although conditional downstream inference remains mixed.

翻译：在高维依赖建模中保持似然可计算性仍然具有挑战性。经典藤Copula方法具有可解释性但计算成本高昂，而许多神经估计器虽灵活但结构欠规范。本文提出摊销去噪Copula（VDC），这是一种通过训练单一双变量去噪模型并跨所有藤边复用的摊销藤Copula框架。针对每条边，模型基于伪观测值预测密度网格，继而采用IPFP/辛克霍恩投影确保非负性、单位质量及均匀边际分布。该方法在保留精确藤似然及经典Copula解释性的同时，以GPU推理替代了逐边重复优化。在合成数据与真实数据基准测试中，VDC展现出优异的双变量密度估计精度、具有竞争力的互信息/总相关估计性能，并实现了高维藤拟合的显著加速。在实际应用中，这些优势使得在传统逐次拟合成本高昂的规模下，显式信息估计与依赖分解成为可能，尽管条件下游推理效果仍参差不齐。