Zirui Zhou

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it
• Optimization & Training — 37 citations into it
• Computational Mechanics — 36 citations into it; #4,966 of 10,001 by trust here, #8,258 by citations
• Federated & Distributed Learning — 33 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Computational Theory and Mathematics — 13 citations into it; #6,063 of 12,182 by trust here, #9,306 by citations
• Statistics and Probability — 13 citations into it

Whom Zirui Zhou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Paul Tseng
2. Jong-Shi Pang
3. Stephen J. Wright
4. Zhi-Quan Tom Luo
5. Yurii E. Nesterov
6. Lin Xiao
7. Alex Krizhevsky
8. Patrick L. Combettes
9. Dimitri P. Bertsekas
10. Andreas Argyriou

Who trusts Zirui Zhou overall

1. Dmitriy Drusvyatskiy
2. Hamed Karimi
3. A. S. Lewis
4. Julie Nutini
5. Deepak Vasisht
6. Mark W. Schmidt

Papers from Zirui Zhou's perspective

Ordered by how strongly the researchers Zirui Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A unified approach to error bounds for structured convex optimization problems (2015) — 200 citations
• Personalized Cross-Silo Federated Learning on Non-IID Data (2020) — 856 citations
• Personalized Federated Learning: An Attentive Collaboration Approach (2020) — 29 citations
• On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization (2013) — 58 citations