5 citations · 8 across the 21 of their papers we have counts for
5 papers · 1 filter
PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance
Taicai Chen, Yue Duan, Dong Li +3
Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, cu…
A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness
Ze Peng, Lei Qi, Yinghuan Shi +1
A recent empirical observation (Li et al., 2022b) of activation sparsity in MLP blocks offers an opportunity to drastically reduce computation costs for free. Although having attri…
Exploring Flat Minima for Domain Generalization with Large Learning Rates
Jian Zhang, Lei Qi, Yinghuan Shi +1
Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A promising approach to improve model generalization in DG is the identification of flat minima. One typi…
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Zekun Li, Lei Qi, Yinghuan Shi +1
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled…
DomainAdaptor: A Novel Approach to Test-time Adaptation
Jian Zhang, Lei Qi, Yinghuan Shi +1
To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificit…