13 citations · 47 across the 6 of their papers we have counts for
6 papers
Visual Prompt Based Personalized Federated Learning
Guanghao Li, Wansen Wu, Yan Sun +3
As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowle…
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Shiwei Liu, Tianlong Chen, Xiaohan Chen +7
Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised a lot of attention currently on post-training pruning (iterative magnitude pruning), and…
Stochastic Client Selection for Federated Learning with Volatile Clients
Tiansheng Huang, Weiwei Lin, Li Shen +2
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public. In each round of synchronous FL training, only a…
MAP Inference via L2-Sphere Linear Program Reformulation
Baoyuan Wu, Li Shen, Tong Zhang +1
Maximum a posteriori (MAP) inference is an important task for graphical models. Due to complex dependencies among variables in realistic model, finding an exact solution for MAP in…
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Shen Li, Christian Häger, Nil Garcia +1
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping…
RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
Shuochao Yao, Yiran Zhao, Huajie Shao +4
Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence to a broad spectrum of mobile and ub…