Andreas ten Pas

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 214 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #8,699 of 21,575 by trust here, #11,498 by citations
• Control and Systems Engineering — 31 citations into it; #1,129 of 8,257 by trust here, #875 by citations
• Aerospace Engineering — 18 citations into it; #5,040 of 6,421 by trust here, #3,280 by citations
• Reinforcement Learning — 16 citations into it
• Biomedical Engineering — 15 citations into it; #2,162 of 10,734 by trust here, #4,472 by citations
• Artificial Intelligence — 13 citations into it; #27,583 of 35,345 by trust here, #30,347 by citations
• Robotics & Control — 11 citations into it
• Human-Computer Interaction — 9 citations into it; #679 of 2,253 by trust here, #978 by citations

Whom Andreas ten Pas trusts overall

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

1. Rosen Diankov
2. Yann LeCun
3. Ross B. Girshick
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. L. Eon Bottou
8. Kaiming He
9. Walter Wohlkinger
10. Joseph Redmon

Who trusts Andreas ten Pas overall

1. Douglas Morrison
2. Marcus Gualtieri
3. Arsalan Mousavian
4. Haoshu Fang
5. Jeffrey Mahler
6. Lucas Manuelli
7. Robert W. Platt
8. Peter Corke
9. Martin Sundermeyer
10. Andy Zeng

Papers from Andreas ten Pas's perspective

Ordered by how strongly the researchers Andreas ten Pas 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. 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

• High precision grasp pose detection in dense clutter (2016) — 327 citations
• Pick and Place Without Geometric Object Models (2017) — 58 citations
• Learning a visuomotor controller for real world robotic grasping using simulated depth images (2017) — 197 citations
• Grasp Pose Detection in Point Clouds (2017) — 673 citations