activity
20122024
most citedMulti-Task Feature Learning Via Efficient l2,1-Norm Minimization

553 citations · 563 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SY2024

Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems

Thanin Quartz, Ruikun Zhou, Hans De Sterck +1

Designing a stabilizing controller for nonlinear systems is a challenging task, especially for high-dimensional problems with unknown dynamics. Traditional reinforcement learning a…

physics.ao-ph20248 cited

FuXi-2.0: Advancing machine learning weather forecasting model for practical applications

Xiaohui Zhong, Lei Chen, Xu Fan +3

Machine learning (ML) models have become increasingly valuable in weather forecasting, providing forecasts that not only lower computational costs but often match or exceed the acc…

cs.CV2024

Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment

Lei Wang, Jun Liu, Liang Zheng +2

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment…

eess.SY2024

LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction

Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1

In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis. The proposed tool,…

eess.SY20241 cited

Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems

Jun Liu, Yiming Meng, Maxwell Fitzsimmons +1

While there has been increasing interest in using neural networks to compute Lyapunov functions, verifying that these functions satisfy the Lyapunov conditions and certifying stabi…

quant-ph20231 cited

From Ad-Hoc to Systematic: A Strategy for Imposing General Boundary Conditions in Discretized PDEs in variational quantum algorithm

Dingjie Lu, Zhao Wang, Jun Liu +3

We proposed a general quantum-computing-based algorithm that harnesses the exponential power of noisy intermediate-scale quantum (NISQ) devices in solving partial differential equa…