14 citations · 17 across the 4 of their papers we have counts for
4 papers
NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing
Tingting Wu, Xiao Ding, Minji Tang +3
Large-scale datasets in the real world inevitably involve label noise. Deep models can gradually overfit noisy labels and thus degrade model generalization. To mitigate the effects…
CC-FedAvg: Computationally Customized Federated Averaging
Hao Zhang, Tingting Wu, Siyao Cheng +1
Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices. It inherently assumes a uniform capacity among…
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination
Tingting Wu, Xiao Ding, Hao Zhang +4
Given data with label noise (i.e., incorrect data), deep neural networks would gradually memorize the label noise and impair model performance. To relieve this issue, curriculum le…
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement
Hao Zhang, Tingting Wu, Siyao Cheng +1
As an emerging technology, federated learning (FL) involves training machine learning models over distributed edge devices, which attracts sustained attention and has been extensiv…