Rudolph Triebel

Artificial Intelligence · 2003–2021

A researcher in the Science corpus: 13 papers; trusts 452 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 195 citations into it; #20,108 of 35,345 by trust here, #21,878 by citations
• Computer Vision and Pattern Recognition — 192 citations into it; #8,996 of 21,575 by trust here, #6,326 by citations
• Probabilistic & Bayesian Methods — 64 citations into it
• Aerospace Engineering — 59 citations into it; #1,878 of 6,421 by trust here, #1,224 by citations
• Optimization & Training — 41 citations into it
• Control and Systems Engineering — 40 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Reinforcement Learning — 27 citations into it

Whom Rudolph Triebel trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Sebastian Thrun
4. Kaiming He
5. Diederik P. Kingma
6. Yoshua Bengio
7. Yann LeCun
8. Pedro F. Felzenszwalb
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Rudolph Triebel overall

1. Asma Azim
2. Bo Li
3. Ryan W. Wolcott
4. Dominic Zeng Wang
5. Daniel Munoz
6. Andrej Karpathy
7. Olivier Aycard
8. Sebastian Thrun
9. B. Douillard
10. Alex Teichman

Papers from Rudolph Triebel's perspective

Ordered by how strongly the researchers Rudolph Triebel 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Implicit 3D Orientation Learning for 6D Object Detection from RGB Images (2018) — 600 citations
• Map building with mobile robots in dynamic environments (2003) — 306 citations
• Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project (2013) — 136 citations
• A system for volumetric robotic mapping of abandoned mines (2003) — 300 citations
• Unknown Object Segmentation from Stereo Images (2021) — 40 citations
• SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports (2015) — 296 citations
• Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes (2021) — 521 citations
• A survey of uncertainty in deep neural networks (2021) — 1,852 citations
• Non-Parametric Calibration for Classification (2019) — 95 citations
• Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks (2016) — 96 citations
• Segmentation and Unsupervised Part-based Discovery of Repetitive Objects (2010) — 70 citations
• Instace-Based AMN Classification for Improved Object Recognition in 2D and 3D Laser Range Data (2007) — 67 citations
• Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing (2006) — 404 citations