Yan Wang

Computer Vision and Pattern Recognition · with Chao · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 526 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 113 citations into it; #11,018 of 21,575 by trust here, #5,711 by citations
• Aerospace Engineering — 39 citations into it; #4,332 of 6,421 by trust here, #1,365 by citations
• Artificial Intelligence — 33 citations into it; #25,096 of 35,345 by trust here, #20,377 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• 3D Vision & Pose — 14 citations into it
• Model Architectures — 12 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Media Technology — 11 citations into it

Whom Yan Wang trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Diederik P. Kingma
7. Karen Simonyan
8. Yann LeCun
9. Ilya Sutskever
10. Shaoqing Ren

Who trusts Yan Wang overall

1. Han-Jia Ye
2. Tai Wang
3. Jonah Philion
4. Shaoshuai Shi
5. Brady Zhou
6. Antreas Antoniou
7. Yuqing Hu
8. Dennis Park
9. Yanwei Li
10. Sourabh Vora

Papers from Yan Wang's perspective

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

Their papers

• Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving (2018) — 1,180 citations
• SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning (2019) — 388 citations
• Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving (2019) — 446 citations
• A Powerful Generative Model Using Random Weights for the Deep Image Representation (2016) — 79 citations