Kuan-Ting Yu

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 51 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it
• Control and Systems Engineering — 24 citations into it; #2,609 of 8,257 by trust here, #631 by citations
• Biomedical Engineering — 16 citations into it; #5,336 of 10,734 by trust here, #3,551 by citations
• Artificial Intelligence — 13 citations into it
• Aerospace Engineering — 11 citations into it; #2,917 of 6,421 by trust here, #1,268 by citations
• Robotics & Control — 7 citations into it; #411 of 609 by trust here, #395 by citations
• Reinforcement Learning — 6 citations into it

Whom Kuan-Ting Yu trusts overall

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

1. Alberto Rodriguez
2. Nikhil Chavan Dafle
3. Francois Robert Hogan
4. Michael Kaess
5. Antonio Torralba
6. Ross B. Girshick
7. Karen Simonyan
8. Matthew Thomas Mason
9. Kaiming He
10. Rob Fergus

Who trusts Kuan-Ting Yu overall

1. Andy Zeng
2. Alberto Rodriguez
3. Maria Bauzá
4. Shuran Song
5. Oliver Kroemer
6. Ajay Mandlekar
7. Ferran Alet
8. Mohit Shridhar
9. Lucas Manuelli
10. Chelsea Finn

Papers from Kuan-Ting Yu's perspective

Ordered by how strongly the researchers Kuan-Ting Yu 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet Large Scale Visual Recognition Challenge
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Their papers

• More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing (2016) — 199 citations
• Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching (2017) — 695 citations
• Realtime State Estimation with Tactile and Visual Sensing. Application to Planar Manipulation (2017) — 42 citations
• Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge (2016) — 481 citations