1 citations · 1 across the 6 of their papers we have counts for
6 papers
SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization
Yingchun Wang, Jingcai Guo, Yi Liu +4
Model substructure learning aims to find an invariant network substructure that can have better out-of-distribution (OOD) generalization than the original full structure. Existing…
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning
Yingchun Wang, Jingcai Guo, Song Guo +1
Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal…
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
Yingchun Wang, Jingcai Guo, Jie Zhang +3
Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in term…
Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning
Yingchun Wang, Jingcai Guo, Song Guo +2
Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model…
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning
Yingchun Wang, Song Guo, Jingcai Guo +4
Network pruning is a promising way to generate light but accurate models and enable their deployment on resource-limited edge devices. However, the current state-of-the-art assumes…
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning
Yi Liu, Song Guo, Jie Zhang +3
To eliminate the requirement of fully-labeled data for supervised model training in traditional Federated Learning (FL), extensive attention has been paid to the application of Sel…