5 citations · 8 across the 3 of their papers we have counts for
3 papers
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.PF2019★ 5 cited
Characterizing Deep Learning Training Workloads on Alibaba-PAI
Mengdi Wang, Chen Meng, Guoping Long +4
Modern deep learning models have been exploited in various domains, including computer vision (CV), natural language processing (NLP), search and recommendation. In practical AI cl…
cs.CV2018★ 3 cited
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks
Mengdi Wang, Qing Zhang, Jun Yang +2
In this work, we propose a graph-adaptive pruning (GAP) method for efficient inference of convolutional neural networks (CNNs). In this method, the network is viewed as a computati…