23 citations · 33 across the 5 of their papers we have counts for
12 papers
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…
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme
Tao Sun, Yuejiao Sun, Dongsheng Li +1
The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective funct…
Decentralized Markov Chain Gradient Descent
Tao Sun, Dongsheng Li
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradie…
Inertial nonconvex alternating minimizations for the image deblurring
Tao Sun, Roberto Barrio, Marcos Rodriguez +1
In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images. One of the most efficient and popular methods to solve the convex TV pr…
Heavy-ball Algorithms Always Escape Saddle Points
Tao Sun, Dongsheng Li, Zhe Quan +3
Nonconvex optimization algorithms with random initialization have attracted increasing attention recently. It has been showed that many first-order methods always avoid saddle poin…