1 citations · 2 across the 5 of their papers we have counts for
5 papers
Nesterov acceleration in optimizing over probability measures
Jiaqi Tang, Qin Li, Wilfrid Gangbo
Optimization over probability measures has become an increasingly important paradigm in modern machine learning, scientific computing, and uncertainty quantification. Motivated by…
A Good Score Does not Lead to A Good Generative Model
Sixu Li, Shi Chen, Qin Li
Score-based Generative Models (SGMs) is one leading method in generative modeling, renowned for their ability to generate high-quality samples from complex, high-dimensional data d…
Bayesian sampling using interacting particles
Shi Chen, Zhiyan Ding, Qin Li
Bayesian sampling is an important task in statistics and machine learning. Over the past decade, many ensemble-type sampling methods have been proposed. In contrast to the classica…
Accelerating optimization over the space of probability measures
Shi Chen, Qin Li, Oliver Tse +1
The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications. While m…
Correcting Auto-Differentiation in Neural-ODE Training
Yewei Xu, Shi Chen, Qin Li
Does the use of auto-differentiation yield reasonable updates for deep neural networks (DNNs)? Specifically, when DNNs are designed to adhere to neural ODE architectures, can we tr…