50 citations · 132 across the 14 of their papers we have counts for
21 papers
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…
Decentralized Federated Averaging
Tao Sun, Dongsheng Li, Bao Wang
Federated averaging (FedAvg) is a communication efficient algorithm for the distributed training with an enormous number of clients. In FedAvg, clients keep their data locally for…
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…
Meta-Learning for Neural Relation Classification with Distant Supervision
Zhenzhen Li, Jian-Yun Nie, Benyou Wang +4
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification. However, the resulting labeled instances are very nois…
Towards Precise End-to-end Weakly Supervised Object Detection Network
Ke Yang, Dongsheng Li, Yong Dou
It is challenging for weakly supervised object detection network to precisely predict the positions of the objects, since there are no instance-level category annotations. Most exi…