71 citations · 172 across the 13 of their papers we have counts for
4 papers · 1 filter
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Jun Shu, Yanwen Zhu, Qian Zhao +2
The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-d…
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels
Jun Shu, Qian Zhao, Zongben Xu +1
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted la…
Learning Adaptive Loss for Robust Learning with Noisy Labels
Jun Shu, Qian Zhao, Keyu Chen +2
Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current robust loss functions, however, inevitably involve hyperparameter(s) t…
Discovering Influential Factors in Variational Autoencoders
Shiqi Liu, Jingxin Liu, Qian Zhao +5
In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract…