50 citations · 213 across the 25 of their papers we have counts for
6 papers · 1 filter
S2 Reducer: High-Performance Sparse Communication to Accelerate Distributed Deep Learning
Keshi Ge, Yongquan Fu, Zhiquan Lai +2
Distributed stochastic gradient descent (SGD) approach has been widely used in large-scale deep learning, and the gradient collective method is vital to ensure the training scalabi…
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…
Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
Baoyun Peng, Min Liu, Zhaoning Zhang +2
Face recognition has made significant progress in recent years due to deep convolutional neural networks (CNN). In many face recognition (FR) scenarios, face images are acquired fr…
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…