Lukas Hewing

Control and Systems Engineering · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 29 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 42 citations into it; #4,097 of 8,257 by trust here, #3,389 by citations
• Artificial Intelligence — 21 citations into it; #35,117 of 35,345 by trust here, #28,481 by citations
• Reinforcement Learning — 12 citations into it
• Computational Theory and Mathematics — 8 citations into it; #12,118 of 12,182 by trust here, #8,294 by citations
• Computer Vision and Pattern Recognition — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Automotive Engineering — 5 citations into it

Whom Lukas Hewing trusts overall

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

1. Joaquin Quiñonero Candela
2. Carl E. Rasmussen
3. Marc Peter Deisenroth
4. C. Ostafew
5. Joel A. E. Andersson
6. Felix Berkenkamp
7. Alexander Liniger
8. Angela P. Schoellig
9. Sanket Kamthe
10. Edward Snelson

Who trusts Lukas Hewing overall

1. Guillem Torrente
2. Shangding Gu
3. Lukas Brunke
4. Juraj Kabzan
5. Henk van Waarde
6. Elia Kaufmann
7. Melissa Greeff
8. M. Kanat Camlibel
9. Philip Föhn
10. Alexander Liniger

Papers from Lukas Hewing's perspective

Ordered by how strongly the researchers Lukas Hewing 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. Gradient-based learning applied to document recognition
5. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Human-level control through deep reinforcement learning

Their papers

• Cautious Model Predictive Control Using Gaussian Process Regression (2017) — 660 citations
• Learning-Based Model Predictive Control: Toward Safe Learning in Control (2020) — 910 citations
• Learning-Based Model Predictive Control for Autonomous Racing (2019) — 448 citations