50 citations · 213 across the 25 of their papers we have counts for
4 papers · 1 filter
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization
Tao Sun, Huaming Ling, Zuoqiang Shi +2
Heavy ball momentum is crucial in accelerating (stochastic) gradient-based optimization algorithms for machine learning. Existing heavy ball momentum is usually weighted by a unifo…
Graph Pooling via Coarsened Graph Infomax
Yunsheng Pang, Yunxiang Zhao, Dongsheng Li
Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning. Existing graph pooling methods either…
Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training
Linbo Qiao, Tao Sun, Hengyue Pan +1
In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training…
XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training
Lei Guan, Wotao Yin, Dongsheng Li +1
We propose XPipe, an efficient asynchronous pipeline model parallelism approach for multi-GPU DNN training. XPipe is designed to use multiple GPUs to concurrently and continuously…