8 papers
Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL
Junyi Wu, Weijian Luo, Haoyang Zheng +2
Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step g…
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
Jiyong Kwon, Ujin Jeon, Sooji Lee +1
Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification and scalability across diverse co…
Ultra Fast PDE Solving via Physics Guided Few-step Diffusion
Cindy Xiangrui Kong, Yueqi Wang, Haoyang Zheng +2
Diffusion-based models have demonstrated impressive accuracy and generalization in solving partial differential equations (PDEs). However, they still face significant limitations,…
Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct
Haoyang Zheng, Xinyang Liu, Cindy Xiangrui Kong +5
Fast and high-quality language generation is the holy grail that people pursue in the age of AI. In this work, we introduce Discrete Diffusion Divergence Instruct (DiDi-Instruct),…
BLADE: Bayesian Langevin Active Discovery with Replica Exchange for Identification of Complex Systems
Cindy Xiangrui Kong, Haoyang Zheng, Guang Lin
Traditional methods for system discovery frequently struggle with efficient data usage and uncertainty quantification. Identifying the governing equations of complex dynamical syst…
Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs
Haoyang Zheng, Bongseok Kim, Guang Lin +1
Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design optimization and digital twins. H…
Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
Haoyang Zheng, Hengrong Du, Ruqi Zhang +1
Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration,…
LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems
Haoyang Zheng, Guang Lin
Sparse Identification of Nonlinear Dynamical Systems (SINDy) is a powerful tool for the data-driven discovery of governing equations. However, it encounters challenges when modelin…