5 citations · 17 across the 14 of their papers we have counts for
7 papers · 1 filter
A Variational Optimal Transport Operator on Incompressible Flow
Jinjin He, Shenyifan Lu, Sinan Wang +3
We present the Variational Incompressible Optimal Transport (VIOT) operator, a generative neural operator for amortized incompressible density transport. Given a new source-target…
Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition
Zhiqi Li, Wen Zhang, Bo Zhu
Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between noise and data. However, these mod…
A Few-Step Generative Model on Cumulative Flow Maps
Zhiqi Li, Duowen Chen, Yuchen Sun +1
We propose a unified, few-step generative modeling framework based on \emph{cumulative flow maps} for long-range transport in probability space, inspired by flow-map techniques for…
Trajectory Consistency for One-Step Generation on Euler Mean Flows
Zhiqi Li, Yuchen Sun, Duowen Chen +2
We propose \emph{Euler Mean Flows (EMF)}, a flow-based generative framework for one-step and few-step generation that enforces long-range trajectory consistency with minimal sampli…
Functional Mean Flow in Hilbert Space
Zhiqi Li, Yuchen Sun, Greg Turk +1
We present Functional Mean Flow (FMF) as a one-step generative model defined in infinite-dimensional Hilbert space. FMF extends the one-step Mean Flow framework to functional domai…
FedGuCci: Making Local Models More Connected in Landscape for Federated Learning
Zexi Li, Jie Lin, Zhiqi Li +5
Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's glo…