Ben Kehoe

Computer Vision and Pattern Recognition · 2013–2015

A researcher in the Science corpus: 4 papers; trusts 95 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #19,379 of 21,575 by trust here, #14,822 by citations
• Control and Systems Engineering — 28 citations into it; #7,067 of 8,257 by trust here, #2,447 by citations
• Artificial Intelligence — 27 citations into it
• Aerospace Engineering — 19 citations into it
• Computer Networks and Communications — 17 citations into it
• Biomedical Engineering — 15 citations into it
• Robotics & Control — 14 citations into it; #500 of 609 by trust here, #384 by citations
• Evaluation & Benchmarks — 11 citations into it

Whom Ben Kehoe trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Antonio Torralba
4. Richard Szeliski
5. Ilya Sutskever
6. David G. Lowe
7. Rob Fergus
8. Jia Deng
9. Yann LeCun
10. Yoshua Bengio

Who trusts Ben Kehoe overall

1. Jeffrey Mahler
2. Lucas Cassano
3. Boyi Liu
4. Joshua Tobin
5. Sergey Levine
6. Pieter Abbeel
7. Kun Yuan
8. Lujia Wang
9. Shixiang Shane Gu
10. Wojciech Zaremba

Papers from Ben Kehoe's perspective

Ordered by how strongly the researchers Ben Kehoe 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Cloud-based robot grasping with the google object recognition engine (2013) — 270 citations
• Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014) — 84 citations
• A Survey of Research on Cloud Robotics and Automation (2015) — 423 citations
• Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014)