Bi-Lipschitz variance-preserving transport maps from Lipschitz scores are L1-dense among all probability densities, with KL convergence for Gaussian convolution targets.
On the universality of coupling-based normalizing flows
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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DBR-AF decouples cross-variable correlations in reconstruction and applies autoregressive flows to model residual densities for improved anomaly detection in multivariate time series.
citing papers explorer
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Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
Bi-Lipschitz variance-preserving transport maps from Lipschitz scores are L1-dense among all probability densities, with KL convergence for Gaussian convolution targets.
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Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation
DBR-AF decouples cross-variable correlations in reconstruction and applies autoregressive flows to model residual densities for improved anomaly detection in multivariate time series.