84 citations · 125 across the 7 of their papers we have counts for
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cs.LG2020★ 2 cited
More Industry-friendly: Federated Learning with High Efficient Design
Dingwei Li, Qinglong Chang, Lixue Pang +4
Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. I…
cs.LG2020★ 20 cited
How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
Gaojie Jin, Xinping Yi, Liang Zhang +3
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability. For fully-connected layers, the…
cs.LG2016
Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But Also Parameters
Zhangyang Wang, Thomas S. Huang
This paper emphasizes the significance to jointly exploit the problem structure and the parameter structure, in the context of deep modeling. As a specific and interesting example,…