Jonathan Scholz

Reinforcement Learning · 2013–2019

A researcher in the Science corpus: 8 papers; trusts 100 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 161 citations into it; #2,061 of 2,242 by trust here, #531 by citations
• Artificial Intelligence — 145 citations into it; #20,394 of 35,345 by trust here, #6,014 by citations
• Computer Vision and Pattern Recognition — 75 citations into it; #14,765 of 21,575 by trust here, #7,750 by citations
• Control and Systems Engineering — 69 citations into it; #4,328 of 8,257 by trust here, #336 by citations
• Robotics & Control — 34 citations into it
• Management Science and Operations Research — 18 citations into it
• Optimization & Training — 11 citations into it
• Generative Models — 9 citations into it

Whom Jonathan Scholz trusts overall

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

1. Marc Peter Deisenroth
2. Diederik P. Kingma
3. Richard S. Sutton
4. Jan Peters
5. Geoffrey E. Hinton
6. Sergey Levine
7. Andrew Y. Ng
8. Yoshua Bengio
9. Duy Nguyen-Tuong
10. Lydia E. Kavraki

Who trusts Jonathan Scholz overall

1. Rico Jonschkowski
2. Abdeslam Boularias
3. Timothée Lesort
4. Debidatta Dwibedi
5. Shaojun Zhu
6. Vikrant Goel
7. Ashvin Nair
8. Sergey Levine
9. Jonathan Tompson
10. Ken Kansky

Papers from Jonathan Scholz's perspective

Ordered by how strongly the researchers Jonathan Scholz 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. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Reinforcement Learning: An Introduction

Their papers

• Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards (2017) — 764 citations
• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) — 160 citations
• PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations (2017) — 68 citations
• A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning (2018) — 118 citations
• A Framework for Data-Driven Robotics (2019) — 30 citations
• Generative predecessor models for sample-efficient imitation learning (2019) — 32 citations
• A Physics-Based Model Prior for Object-Oriented MDPs (2014) — 67 citations
• Policy Shaping: Integrating Human Feedback with Reinforcement Learning (2013) — 484 citations