1 citations · 1 across the 5 of their papers we have counts for
5 papers
AlignFlow: Improving Flow-based Generative Models with Semi-Discrete Optimal Transport
Lingkai Kong, Molei Tao, Yang Liu +4
Flow-based Generative Models (FGMs) effectively transform noise into complex data distributions. Incorporating Optimal Transport (OT) to couple noise and data during FGM training h…
DeepTTV: Deep Learning Prediction of Hidden Exoplanet From Transit Timing Variations
Chen Chen, Lingkai Kong, Gongjie Li +1
Transit timing variation (TTV) provides rich information about the mass and orbital properties of exoplanets, which are often obtained by solving an inverse problem via Markov Chai…
Quantitative Convergences of Lie Group Momentum Optimizers
Lingkai Kong, Molei Tao
Explicit, momentum-based dynamics that optimize functions defined on Lie groups can be constructed via variational optimization and momentum trivialization. Structure preserving ti…
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
Yuchen Zhu, Tianrong Chen, Lingkai Kong +2
The generative modeling of data on manifolds is an important task, for which diffusion models in flat spaces typically need nontrivial adaptations. This article demonstrates how a…
Convergence of Kinetic Langevin Monte Carlo on Lie groups
Lingkai Kong, Molei Tao
Explicit, momentum-based dynamics for optimizing functions defined on Lie groups was recently constructed, based on techniques such as variational optimization and left trivializat…