Diffusion models (DMs) have gained attention in Missing Data Imputation (MDI), but there
remain two long-neglected issues to be addressed:(1). Inaccurate Imputation, which arises …

幻灯片 1 Page 1 NeurIPS 2024, Main Track, Submission ID: #1850 Rethinking the Diffusion
Models for Missing Data Imputation: A Gradient Flow Perspective Presenter:Zhichao Chen …

Bayesian inversion is central to the quantification of uncertainty within problems arising from
numerous applications in science and engineering. To formulate the approach, four …

We study the geometry of conditional optimal transport (COT) and prove a dynamical
formulation which generalizes the Benamou-Brenier Theorem. With these tools, we propose …

Given a forward mapping Φ: RN→ RM, the region {x∈ RN,∥ Φ (x)-y∥ 2≤ ε}, where y∈ RM
is a given vector and ε≥ 0 is a perturbation amplitude, represents the set of all possible …

Wasserstein distances greatly influenced and coined various types of generative neural
network models. Wasserstein autoencoders are particularly notable for their mathematical …

The possibility of using the Eulerian discretization for the problem of modelling high-
dimensional distributions and sampling, is studied. The problem is posed as a minimization …

Contrast enhancement by Gadolinium-based contrast agents (GBCAs) is a vital tool for
tumor diagnosis in neuroradiology. Based on brain MRI scans of glioblastoma before and …