Bi-Lipschitz variance-preserving transport maps from Lipschitz scores are L1-dense among all probability densities, with KL convergence for Gaussian convolution targets.
arXiv preprint arXiv:2402.06578 (2024)
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
End-to-end masked-image VAE plus normalizing flow yields FID 2.50 on ImageNet 256 with 128 tokens and higher linear-probe accuracy than unmasked counterparts.
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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Variational Sequential Optimal Experimental Design using Reinforcement Learning
vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
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MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation
End-to-end masked-image VAE plus normalizing flow yields FID 2.50 on ImageNet 256 with 128 tokens and higher linear-probe accuracy than unmasked counterparts.
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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.
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