5 papers
Structure-Aware Variational Learning of a Class of Generalized Diffusions
Yubin Lu, Xiaofan Li, Chun Liu +2
Learning the underlying potential energy of stochastic gradient systems from partial and noisy observations is a fundamental problem arising in physics, chemistry, and data-driven…
When Noise Lowers The Loss: Rethinking Likelihood-Based Evaluation in Music Large Language Models
Xiaosha Li, Chun Liu, Ziyu Wang
The rise of music large language models (LLMs) demands robust methods of evaluating output quality, especially in distinguishing high-quality compositions from "garbage music". Cur…
Unified Learning of the Profile Function in Discrete Keller-Segel Models
Chi-An Chen, Chun Liu, Ming Zhong
We propose a unified learning framework for identifying the profile function in discrete Keller-Segel equations, which are widely used mathematical models for understanding chemota…
Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
Wu Yichao, Wang Yirui, Ding Panpan +3
With the wide application of deep reinforcement learning (DRL) techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to impr…
Stability in Training PINNs for Stiff PDEs: Why Initial Conditions Matter
Baoli Hao, Ulisses Braga-Neto, Chun Liu +2
Training physics-informed neural networks (PINNs) on stiff, time-dependent PDEs remains a fundamental challenge due to optimization instabilities and gradient pathologies. Through…