Canyi Lu

Computer Vision and Pattern Recognition · 2012–2018

A researcher in the Science corpus: 8 papers; trusts 249 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it; #14,198 of 21,575 by trust here, #10,154 by citations
• Computational Mechanics — 90 citations into it; #3,709 of 10,001 by trust here, #3,190 by citations
• Artificial Intelligence — 44 citations into it; #31,109 of 35,345 by trust here, #33,294 by citations
• Computational Mathematics — 34 citations into it; #480 of 612 by trust here, #389 by citations
• Signal Processing — 23 citations into it; #6,136 of 7,572 by trust here, #4,900 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Computational Theory and Mathematics — 20 citations into it
• Statistics and Probability — 17 citations into it

Whom Canyi Lu trusts overall

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

1. E.J. Candes
2. Guangcan Liu
3. Jerome H. Friedman
4. Tong Zhang
5. Cun-Hui Zhang
6. Misha Elena Kilmer
7. Zhouchen Lin
8. B. Recht
9. Alex Krizhevsky
10. Yi Ma

Who trusts Canyi Lu overall

1. Landan Zhang
2. Raymond A. Yeh
3. Yimian Dai
4. Jiashi Feng
5. Zhenming Peng
6. Qi Xie
7. Zhouchen Lin
8. Yuan Xie
9. Yang Sun
10. Chong You

Papers from Canyi Lu's perspective

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

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

Their papers

• Robust and Efficient Subspace Segmentation via Least Squares Regression (2012) — 707 citations
• Generalized Nonconvex Nonsmooth Low-Rank Minimization (2014) — 292 citations
• Cross-Modal Retrieval With CNN Visual Features: A New Baseline (2017) — 370 citations
• Tensor Factorization for Low-Rank Tensor Completion (2018) — 345 citations
• Subspace Clustering by Block Diagonal Representation (2018) — 436 citations
• Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm (2018) — 988 citations
• Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization (2016) — 528 citations
• Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm (2015) — 336 citations