229 citations · 478 across the 21 of their papers we have counts for
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cs.LG2023
A Game-theoretic Framework for Privacy-preserving Federated Learning
Xiaojin Zhang, Lixin Fan, Siwei Wang +3
In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of \textit{privacy leakage} cannot be ignored in the presence of \texti…
cs.LG2020★ 1 cited
How Does BN Increase Collapsed Neural Network Filters?
Sheng Zhou, Xinjiang Wang, Ping Luo +3
Improving sparsity of deep neural networks (DNNs) is essential for network compression and has drawn much attention. In this work, we disclose a harmful sparsifying process called…
cs.LG2016★ 229 cited
Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob +2
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue t…