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cs.LG2021
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Jiandong Mu, Mengdi Wang, Feiwen Zhu +3
Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for appli…
cs.LG2020
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling
Jun Yang, Fei Wang
Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. H…