31 citations · 32 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Adaptive pruning-based Newton's method for distributed learning
Shuzhen Chen, Yuan Yuan, Youming Tao +3
Newton's method leverages curvature information to boost performance, and thus outperforms first-order methods for distributed learning problems. However, Newton's method is not pr…
cs.SI2023★ 31 cited
Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach
Zhongwei Zhan, Yingjie Wang, Peiyong Duan +6
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be…
cs.CV2022★ 1 cited
Improving information retention in large scale online continual learning
Zhipeng Cai, Vladlen Koltun, Ozan Sener
Given a stream of data sampled from non-stationary distributions, online continual learning (OCL) aims to adapt efficiently to new data while retaining existing knowledge. The typi…