Urs Muller

Computer Vision and Pattern Recognition · 1994–2018

A researcher in the Science corpus: 12 papers; trusts 199 researchers and is trusted by 205 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #2,676 of 21,575 by trust here, #1,157 by citations
• Artificial Intelligence — 55 citations into it; #17,585 of 35,345 by trust here, #2,425 by citations
• Safety, Robustness & Interpretability — 37 citations into it
• Model Architectures — 16 citations into it
• Aerospace Engineering — 11 citations into it; #3,678 of 6,421 by trust here, #1,134 by citations
• Biophysics — 11 citations into it
• Statistical Learning Theory — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Urs Muller trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Suzanna Becker
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. Kaiming He
8. Karen Simonyan
9. Matthew D. Zeiler
10. Ross B. Girshick

Who trusts Urs Muller overall

1. Lei Tai
2. L. Eon Bottou
3. Yann LeCun
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Alexey Dosovitskiy
7. Vladlen Koltun
8. Sergey Levine
9. Lu Chi
10. Kyunghyun Cho

Papers from Urs Muller's perspective

Ordered by how strongly the researchers Urs Muller 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• End to End Learning for Self-Driving Cars (2016) — 4,658 citations
• Learning long‐range vision for autonomous off‐road driving (2009) — 320 citations
• Comparison of learning algorithms for handwritten digit recognition (1995)
• Learning algorithms for classification: A comparison on handwritten digit recognition (1995)
• Learning long-range vision for autonomous off-road driving (2009) — 123 citations
• Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car (2017) — 437 citations
• Deep belief net learning in a long-range vision system for autonomous off-road driving (2008) — 100 citations
• VisualBackProp: visualizing CNNs for autonomous driving (2016) — 78 citations
• VisualBackProp: efficient visualization of CNNs (2018) — 56 citations
• Off-Road Obstacle Avoidance through End-to-End Learning (2005) — 623 citations
• Fast neural net simulation with a DSP processor array (1995)
• Comparison of classifier methods: a case study in handwritten digit recognition (1994)