Fangchang Ma

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 466 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it; #6,189 of 21,575 by trust here, #10,677 by citations
• Media Technology — 25 citations into it; #2,738 of 3,621 by trust here, #1,923 by citations
• Aerospace Engineering — 17 citations into it; #2,075 of 6,421 by trust here, #2,734 by citations
• Artificial Intelligence — 17 citations into it
• Optimization & Training — 10 citations into it
• Generative Models — 9 citations into it; #1,029 of 1,956 by trust here, #1,635 by citations
• Model Architectures — 8 citations into it
• 3D Vision & Pose — 7 citations into it

Whom Fangchang Ma trusts overall

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

1. Kaiming He
2. Yann LeCun
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. X. Zhang
6. Yoshua Bengio
7. Ashutosh Saxena
8. Diederik P. Kingma
9. Rob Fergus
10. Shaoqing Ren

Who trusts Fangchang Ma overall

1. Yujun Shen
2. Xinjing Cheng
3. Jie Tang
4. Jiaxiong Qiu
5. Jinjin Gu
6. Junjie Hu
7. Bolei Zhou
8. Fredrik K. Gustafsson
9. Jinsun Park
10. Yaodong Cui

Papers from Fangchang Ma's perspective

Ordered by how strongly the researchers Fangchang Ma 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• FastDepth: Fast Monocular Depth Estimation on Embedded Systems (2019) — 373 citations
• Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image (2017) — 667 citations
• Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera (2018) — 460 citations
• Invertibility of Convolutional Generative Networks from Partial Measurements (2018) — 70 citations