Tzu-Mao Li

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 51 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 32 citations into it; #12,875 of 21,575 by trust here, #10,677 by citations
• Computer Graphics and Computer-Aided Design — 18 citations into it; #899 of 1,584 by trust here, #526 by citations
• Artificial Intelligence — 17 citations into it
• Computational Mechanics — 14 citations into it
• Generative Models — 9 citations into it; #1,415 of 1,956 by trust here, #1,843 by citations
• Optimization & Training — 8 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Tzu-Mao Li trusts overall

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

1. Diederik P. Kingma
2. Aaron Hertzmann
3. Andreas Griewank
4. Geoffrey E. Hinton
5. Individualized Treat
6. Alex Krizhevsky
7. Jimmy Ba
8. Yoshua Bengio
9. Phillip Isola
10. Yann LeCun

Who trusts Tzu-Mao Li overall

1. Yingtao Tian
2. I-Chao Shen
3. Kevin Frans
4. Peter Schaldenbrand
5. Pradyumna Reddy
6. Zeng Huang
7. Thu Nguyen-Phuoc
8. Kai Zhang
9. Chrisantha Fernando
10. Michael Gharbi

Papers from Tzu-Mao Li's perspective

Ordered by how strongly the researchers Tzu-Mao Li 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Differentiable Monte Carlo ray tracing through edge sampling (2018) — 586 citations
• Differentiable vector graphics rasterization for editing and learning (2020) — 340 citations
• DiffTaichi: Differentiable Programming for Physical Simulation (2019) — 516 citations