1 citations · 1 across the 6 of their papers we have counts for
7 papers
Shapley Uncertainty in Natural Language Generation
Meilin Zhu, Gaojie Jin, Xiaowei Huang +1
In question-answering tasks, determining when to trust the outputs is crucial to the alignment of large language models (LLMs). Kuhn et al. (2023) introduces semantic entropy as a…
ThermoRL:Structure-Aware Reinforcement Learning for Protein Mutation Design to Enhance Thermostability
Xiangwen Wang, Gaojie Jin, Xiaowei Huang +1
Designing mutations to optimize protein thermostability remains challenging due to the complex relationship between sequence variations, structural dynamics, and thermostability, o…
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
Sihao Wu, Xiaonan Si, Chi Xing +5
The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrat…
Training Verification-Friendly Neural Networks via Neuron Behavior Consistency
Zongxin Liu, Zhe Zhao, Fu Song +4
Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel m…
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
Gaojie Jin, Ronghui Mu, Xinping Yi +2
The Invariant Risk Minimization (IRM) approach aims to address the challenge of domain generalization by training a feature representation that remains invariant across multiple en…
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds
Hanwei Zhang, Luo Cheng, Qisong He +6
Classification of 3D point clouds is a challenging machine learning (ML) task with important real-world applications in a spectrum from autonomous driving and robot-assisted surger…
DeepCDCL: An CDCL-based Neural Network Verification Framework
Zongxin Liu, Pengfei Yang, Lijun Zhang +1
Neural networks in safety-critical applications face increasing safety and security concerns due to their susceptibility to little disturbance. In this paper, we propose DeepCDCL,…