5 citations · 21 across the 7 of their papers we have counts for
4 papers · 1 filter
A Fast, Performant, Secure Distributed Training Framework For Large Language Model
Wei Huang, Yinggui Wang, Anda Cheng +3
The distributed (federated) LLM is an important method for co-training the domain-specific LLM using siloed data. However, maliciously stealing model parameters and data from the s…
Enhancing Adversarial Training with Second-Order Statistics of Weights
Gaojie Jin, Xinping Yi, Wei Huang +2
Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over mo…
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
Wei Huang, Chunrui Liu, Yilan Chen +2
PAC-Bayesian is an analysis framework where the training error can be expressed as the weighted average of the hypotheses in the posterior distribution whilst incorporating the pri…
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Xingyu Zhao, Wei Huang, Alec Banks +4
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications. While it shows great potential to provide transformational capabilities, DL al…