12 citations · 36 across the 8 of their papers we have counts for
10 papers
Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering
Haiwei Wu, Yiming Chen, Jiantao Zhou +1
Image forgery detection aims to detect and locate forged regions in an image. Most existing forgery detection algorithms formulate classification problems to classify pixels into f…
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
Yutong He, Xinmeng Huang, Yiming Chen +2
Communication compression is an essential strategy for alleviating communication overhead by reducing the volume of information exchanged between computing nodes in large-scale dis…
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Kun Yuan, Xinmeng Huang, Yiming Chen +3
Decentralized optimization is effective to save communication in large-scale machine learning. Although numerous algorithms have been proposed with theoretical guarantees and empir…
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Xinmeng Huang, Yiming Chen, Wotao Yin +1
Recent advances in distributed optimization and learning have shown that communication compression is one of the most effective means of reducing communication. While there have be…
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Bicheng Ying, Kun Yuan, Hanbin Hu +2
Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On larg…
Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying, Kun Yuan, Yiming Chen +3
Decentralized SGD is an emerging training method for deep learning known for its much less (thus faster) communication per iteration, which relaxes the averaging step in parallel S…
Bias Loss for Mobile Neural Networks
Lusine Abrahamyan, Valentin Ziatchin, Yiming Chen +1
Compact convolutional neural networks (CNNs) have witnessed exceptional improvements in performance in recent years. However, they still fail to provide the same predictive power a…
Accelerating Gossip SGD with Periodic Global Averaging
Yiming Chen, Kun Yuan, Yingya Zhang +3
Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient th…
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
Kun Yuan, Yiming Chen, Xinmeng Huang +4
The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is…
Learned Gradient Compression for Distributed Deep Learning
Lusine Abrahamyan, Yiming Chen, Giannis Bekoulis +1
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed tra…