VDC amortizes vine copula construction by reusing a single trained denoising model across edges plus IPFP projection, yielding competitive density and mutual information estimates with faster high-dimensional fitting.
Normalizing flows for probabilistic modeling and inference
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Causal-Adapter adapts frozen diffusion backbones via structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to achieve faithful counterfactual generation with strong attribute control and identity preservation.
EventFlow applies flow matching to learn joint distributions over event times for temporal point processes, reporting 20-53% lower forecast error than autoregressive baselines on standard TPP benchmarks with fewer sampling calls.
citing papers explorer
-
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
VDC amortizes vine copula construction by reusing a single trained denoising model across edges plus IPFP projection, yielding competitive density and mutual information estimates with faster high-dimensional fitting.
-
Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Causal-Adapter adapts frozen diffusion backbones via structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to achieve faithful counterfactual generation with strong attribute control and identity preservation.
-
EventFlow: Forecasting Temporal Point Processes with Flow Matching
EventFlow applies flow matching to learn joint distributions over event times for temporal point processes, reporting 20-53% lower forecast error than autoregressive baselines on standard TPP benchmarks with fewer sampling calls.