Elia Kaufmann

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #21,446 of 21,575 by trust here, #13,815 by citations
• Aerospace Engineering — 55 citations into it
• Artificial Intelligence — 33 citations into it
• Control and Systems Engineering — 27 citations into it
• Robotics & Control — 26 citations into it
• Reinforcement Learning — 20 citations into it
• Ocean Engineering — 8 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Elia Kaufmann trusts overall

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

1. Daniel Mellinger
2. Davide Falanga
3. Vijay R. Kumar
4. Richard S. Sutton
5. Fadri Furrer
6. Stéphane Ross
7. Giuseppe Loianno
8. Antonio Loquercio
9. Davide Scaramuzza
10. Matthias Faessler

Who trusts Elia Kaufmann overall

1. Antonio Loquercio
2. Somil Bansal
3. Ananye Agarwal
4. Michael O'Connell
5. Mattia Segu
6. Chuang Gan
7. Davide Scaramuzza
8. Jitendra Malik
9. Varun Tolani

Papers from Elia Kaufmann's perspective

Ordered by how strongly the researchers Elia Kaufmann 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing (2018) — 136 citations
• Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018) — 154 citations
• Champion-level drone racing using deep reinforcement learning (2023) — 875 citations
• Data-Driven MPC for Quadrotors (2021) — 304 citations
• Learning high-speed flight in the wild (2021) — 449 citations