Peter Karkus

Reinforcement Learning · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 159 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 60 citations into it
• Artificial Intelligence — 59 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Aerospace Engineering — 28 citations into it
• Control and Systems Engineering — 22 citations into it
• Robotics & Control — 16 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Electrical and Electronic Engineering — 7 citations into it

Whom Peter Karkus trusts overall

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

1. Diederik P. Kingma
2. Raul Mur-Artal
3. Geoffrey E. Hinton
4. Michael Montemerlo
5. Alex Krizhevsky
6. Kaiming He
7. Yoshua Bengio
8. Sebastian Thrun
9. Yann LeCun
10. Saurabh Gupta

Who trusts Peter Karkus overall

1. Santhosh K. Ramakrishnan

Papers from Peter Karkus's perspective

Ordered by how strongly the researchers Peter Karkus 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation (2021) — 70 citations
• Physically Embedded Planning Problems: New Challenges for Reinforcement Learning (2020) — 14 citations