David Barker

Reinforcement Learning · with Scholz · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 107 citations into it
• Artificial Intelligence — 102 citations into it; #26,702 of 35,345 by trust here, #11,750 by citations
• Control and Systems Engineering — 51 citations into it; #6,967 of 8,257 by trust here, #1,252 by citations

Whom David Barker trusts overall

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

1. Sergey Levine
2. Chelsea Finn
3. Marc G. Bellemare
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Richard S. Sutton

Who trusts David Barker overall

1. Danfei Xu

Papers from David Barker's perspective

Ordered by how strongly the researchers David Barker 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. ImageNet Large Scale Visual Recognition Challenge
4. Human-level control through deep reinforcement learning
5. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Attention is All you Need (Neural Information Processing Systems)
10. Layer Normalization

Their papers

• Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) — 160 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