Sebastian Schulze

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 47 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #21,491 of 35,345 by trust here, #19,873 by citations
• Reinforcement Learning — 45 citations into it
• Management Science and Operations Research — 21 citations into it
• Control and Systems Engineering — 9 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Sebastian Schulze trusts overall

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

1. Arthur Guez
2. Richard S. Sutton
3. David Silver
4. Ian Osband
5. Volodymyr Kuleshov
6. Mohammad Ghavamzadeh
7. Richard Dearden
8. Diederik P. Kingma
9. Geoffrey E. Hinton
10. Andrew Y. Ng

Who trusts Sebastian Schulze overall

1. Jan Leike
2. David Krueger
3. Ron Dorfman
4. Annie Xie

Papers from Sebastian Schulze's perspective

Ordered by how strongly the researchers Sebastian Schulze 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

• VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (2019) — 331 citations
• Active Reinforcement Learning with Monte-Carlo Tree Search (2018) — 20 citations