Felix Berkenkamp

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #9,379 of 35,345 by trust here, #6,614 by citations
• Reinforcement Learning — 50 citations into it; #928 of 2,242 by trust here, #669 by citations
• Control and Systems Engineering — 33 citations into it; #2,031 of 8,257 by trust here, #923 by citations
• Computational Theory and Mathematics — 29 citations into it
• Management Science and Operations Research — 16 citations into it
• Optimization & Training — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Biomedical Engineering — 6 citations into it

Whom Felix Berkenkamp trusts overall

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

1. Richard S. Sutton
2. Teodor Mihai Moldovan
3. Jan Peters
4. Pieter Abbeel
5. Andrew Barto
6. Andreas Krause
7. P. Pardalos
8. Christopher K. I. Williams
9. Scott Davies
10. Geoffrey E. Hinton

Who trusts Felix Berkenkamp overall

1. Lukas Hewing
2. Sergey Levine
3. Guanya Shi
4. Lukas Brunke
5. Richard Cheng
6. Gabriel Dulac-Arnold
7. Andreas Krause
8. Stefan Depeweg
9. Yinlam Chow
10. Shangding Gu

Papers from Felix Berkenkamp's perspective

Ordered by how strongly the researchers Felix Berkenkamp 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning (2020) — 303 citations
• Safe Model-based Reinforcement Learning with Stability Guarantees (2017) — 1,021 citations
• Safe Exploration in Finite Markov Decision Processes with Gaussian Processes (2016) — 204 citations
• Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics (2016) — 364 citations
• Learning-Based Model Predictive Control for Safe Exploration (2018) — 439 citations
• Safe controller optimization for quadrotors with Gaussian processes (2015) — 335 citations