Zhiqin Lu

Artificial Intelligence · 2000–2016

A researcher in the Science corpus: 7 papers; trusts 29 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #22,078 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #13,674 of 21,575 by trust here, #15,162 by citations
• Geometry and Topology — 13 citations into it; #457 of 7,422 by trust here, #1,451 by citations
• Applied Mathematics — 12 citations into it; #429 of 7,620 by trust here, #1,522 by citations
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Computational Mechanics — 6 citations into it
• Astronomy and Astrophysics — 5 citations into it; #10,278 of 10,964 by trust here, #10,839 by citations

Whom Zhiqin Lu trusts overall

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

1. Steve Zelditch
2. Albrecht Böttcher
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. K. Hirachi
6. K. Yoshikawa
7. Shun-ichi Amari
8. Yann LeCun
9. Pierre Baldi
10. Alex Krizhevsky

Who trusts Zhiqin Lu overall

1. Duong H. Phong
2. Xiaowei Wang
3. Kento Fujita
4. L. Charles
5. Jacob Sturm
6. Simon Donaldson
7. G'abor Sz'ekelyhidi
8. Mehdi Lejmi
9. T. Mabuchi
10. X. Dai

Papers from Zhiqin Lu's perspective

Ordered by how strongly the researchers Zhiqin 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Going deeper with convolutions
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning in the Machine: Random Backpropagation and the Deep Learning Channel (2016) — 72 citations
• Complex-Valued Autoencoders (2011) — 63 citations
• Normal Scalar Curvature Conjecture and its applications (2008) — 145 citations
• ANALYTIC TORSION FOR CALABI-YAU THREEFOLDS (2006) — 63 citations
• On the DDVV conjecture and the comass in calibrated geometry (I) (2006) — 65 citations
• The log term of Szego Kernel (2004) — 65 citations
• On the lower order terms of the asymptotic expansion of Tian-Yau-Zelditch (2000) — 287 citations