52 citations · 179 across the 13 of their papers we have counts for
6 papers · 2 filters
A Novel Convergence Analysis for Algorithms of the Adam Family
Zhishuai Guo, Yi Xu, Wotao Yin +2
Since its invention in 2014, the Adam optimizer has received tremendous attention. On one hand, it has been widely used in deep learning and many variants have been proposed, while…
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
While semi-supervised learning (SSL) has received tremendous attentions in many machine learning tasks due to its successful use of unlabeled data, existing SSL algorithms use eith…
Why Does Multi-Epoch Training Help?
Yi Xu, Qi Qian, Hao Li +1
Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in e…
A Theoretical Analysis of Learning with Noisily Labeled Data
Yi Xu, Qi Qian, Hao Li +1
Noisy labels are very common in deep supervised learning. Although many studies tend to improve the robustness of deep training for noisy labels, rare works focus on theoretically…
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks
Asaf Noy, Yi Xu, Yonathan Aflalo +2
Deep neural networks' remarkable ability to correctly fit training data when optimized by gradient-based algorithms is yet to be fully understood. Recent theoretical results explai…
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
Zhuoning Yuan, Zhishuai Guo, Yi Xu +2
Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on…