Matthias Müller

Computer Vision and Pattern Recognition · Science Robotics · 2017–2023

A researcher in the Science corpus: 11 papers; trusts 801 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 265 citations into it; #6,519 of 21,575 by trust here, #4,438 by citations
• Artificial Intelligence — 197 citations into it; #12,319 of 35,345 by trust here, #5,834 by citations
• Reinforcement Learning — 80 citations into it; #1,025 of 2,242 by trust here, #728 by citations
• Aerospace Engineering — 68 citations into it
• Graph Learning — 50 citations into it
• Control and Systems Engineering — 48 citations into it; #2,207 of 8,257 by trust here, #585 by citations
• Model Architectures — 43 citations into it
• Robotics & Control — 42 citations into it; #165 of 609 by trust here, #146 by citations

Whom Matthias Müller trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yann LeCun
6. Yoshua Bengio
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Jimmy Ba

Who trusts Matthias Müller overall

1. Martin Danelljan
2. Antonio Loquercio
3. Heng Fan
4. Xin Chen
5. Goutam Bhat
6. Fredrik K. Gustafsson
7. Felipe Codevilla
8. Ning Wang
9. Mayank Bansal
10. Luc Van Gool

Papers from Matthias Müller's perspective

Ordered by how strongly the researchers Matthias Müller 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
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

• TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild (2018) — 1,118 citations
• End-to-End Driving Via Conditional Imitation Learning (2017) — 1,304 citations
• Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications (2017) — 203 citations
• DeepGCNs: Can GCNs Go As Deep As CNNs? (2019) — 1,623 citations
• SGAS: Sequential Greedy Architecture Search (2019) — 210 citations
• Champion-level drone racing using deep reinforcement learning (2023) — 875 citations
• Reaching the limit in autonomous racing: Optimal control versus reinforcement learning (2023) — 300 citations
• Learning high-speed flight in the wild (2021) — 449 citations
• Training Graph Neural Networks with 1000 Layers (2021) — 302 citations
• Driving Policy Transfer via Modularity and Abstraction (2018) — 245 citations
• Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation (2017) — 34 citations