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of 158
pro vyhledávání: '"Tong, Qianqian"'
Stochastic optimization algorithms are widely used for large-scale data analysis due to their low per-iteration costs, but they often suffer from slow asymptotic convergence caused by inherent variance. Variance-reduced techniques have been therefore
Externí odkaz:
http://arxiv.org/abs/2407.16968
Autor:
Du, Nan, Jia, Guorong, Zhang, Wen, Tong, Qianqian, Qu, Xudong, Liu, Rong, Li, Danni, Yan, Zhiping, Zuo, Changjing, Li, Xiao, Li, Rou, Zhang, Wei
Publikováno v:
In Heliyon 15 May 2024 10(9)
Stochastically controlled stochastic gradient (SCSG) methods have been proved to converge efficiently to first-order stationary points which, however, can be saddle points in nonconvex optimization. It has been observed that a stochastic gradient des
Externí odkaz:
http://arxiv.org/abs/2103.04413
Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex sparse learning due to their capability of recov
Externí odkaz:
http://arxiv.org/abs/2101.00052
Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effect
Externí odkaz:
http://arxiv.org/abs/2011.00667
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential roles in sparse learning, and have drawn extensive attentions recently.
Externí odkaz:
http://arxiv.org/abs/2009.06562
Federated learning allows loads of edge computing devices to collaboratively learn a global model without data sharing. The analysis with partial device participation under non-IID and unbalanced data reflects more reality. In this work, we propose f
Externí odkaz:
http://arxiv.org/abs/2009.06557
Autor:
Xiong, Zhaohan, Xia, Qing, Hu, Zhiqiang, Huang, Ning, Bian, Cheng, Zheng, Yefeng, Vesal, Sulaiman, Ravikumar, Nishant, Maier, Andreas, Yang, Xin, Heng, Pheng-Ann, Ni, Dong, Li, Caizi, Tong, Qianqian, Si, Weixin, Puybareau, Elodie, Khoudli, Younes, Geraud, Thierry, Chen, Chen, Bai, Wenjia, Rueckert, Daniel, Xu, Lingchao, Zhuang, Xiahai, Luo, Xinzhe, Jia, Shuman, Sermesant, Maxime, Liu, Yashu, Wang, Kuanquan, Borra, Davide, Masci, Alessandro, Corsi, Cristiana, de Vente, Coen, Veta, Mitko, Karim, Rashed, Preetha, Chandrakanth Jayachandran, Engelhardt, Sandy, Qiao, Menyun, Wang, Yuanyuan, Tao, Qian, Nunez-Garcia, Marta, Camara, Oscar, Savioli, Nicolo, Lamata, Pablo, Zhao, Jichao
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmenta
Externí odkaz:
http://arxiv.org/abs/2004.12314
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