Junyu Zhang
Junyu Zhang
Verified email at umn.edu
TitleCited byYear
Highly accurate model for prediction of lung nodule malignancy with CT scans
JL Causey, J Zhang, S Ma, B Jiang, JA Qualls, DG Politte, F Prior, ...
Scientific reports 8 (1), 9286, 2018
312018
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis
J Zhang, S Ma, S Zhang
Mathematical Programming (Series A), https://doi.org/10.1007/s10107-019-01418-8, 2019
112019
A Cubic Regularized Newton's Method over Riemannian Manifolds
J Zhang, S Zhang
arXiv preprint arXiv:1805.05565, 2018
82018
Adaptive stochastic variance reduction for subsampled Newton method with cubic regularization
J Zhang, L Xiao, S Zhang
arXiv preprint arXiv:1811.11637, 2018
72018
A sparse completely positive relaxation of the modularity maximization for community detection
J Zhang, H Liu, Z Wen, S Zhang
SIAM Journal on Scientific Computing 40 (5), A3091-A3120, 2018
42018
Subspace methods with local refinements for eigenvalue computation using low-rank tensor-train format
J Zhang, Z Wen, Y Zhang
Journal of Scientific Computing 70 (2), 478-499, 2017
42017
A Composite Randomized Incremental Gradient Method
J Zhang, L Xiao
International Conference on Machine Learning, 7454-7462, 2019
32019
Multi-level composite stochastic optimization via nested variance reduction
J Zhang, L Xiao
arXiv preprint arXiv:1908.11468, 2019
22019
A Stochastic Composite Gradient Method with Incremental Variance Reduction
J Zhang, L Xiao
arXiv preprint arXiv:1906.10186, 2019
22019
From low probability to high confidence in stochastic convex optimization
D Davis, D Drusvyatskiy, L Xiao, J Zhang
arXiv preprint arXiv:1907.13307, 2019
1*2019
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks
L Wang, W Wu, Y Zhao, J Zhang, H Liu, G Bosilca, J Dongarra, M Herlihy, ...
arXiv preprint arXiv:1811.08596, 2018
2018
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