Klaus Obermayer

Reinforcement Learning · 2002–2015

A researcher in the Science corpus: 10 papers; trusts 75 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 68 citations into it
• Artificial Intelligence — 43 citations into it; #22,348 of 35,345 by trust here, #12,761 by citations
• Control and Systems Engineering — 16 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #2,962 of 3,145 by trust here, #2,805 by citations
• Cognitive Neuroscience — 13 citations into it; #8,522 of 10,852 by trust here, #3,802 by citations
• Statistical Learning Theory — 11 citations into it
• Molecular Biology — 10 citations into it; #5,624 of 17,773 by trust here, #12,008 by citations

Whom Klaus Obermayer trusts overall

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

1. Sepp Hochreiter
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Dimitri P. Bertsekas
5. Andrew Barto
6. Satinder Singh
7. Steven J. Bradtke
8. J. Urgen Schmidhuber
9. Yoshua Bengio
10. Bernhard Schölkopf

Who trusts Klaus Obermayer overall

1. José Antonio Arjona-Medina
2. Thomas Unterthiner
3. Alex Graves
4. Michael Widrich
5. Junjie Chen
6. Sepp Hochreiter
7. Michael Gillhofer
8. J. Urgen Schmidhuber

Papers from Klaus Obermayer's perspective

Ordered by how strongly the researchers Klaus Obermayer 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Reinforcement Learning: An Introduction
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Risk-Sensitive Reinforcement Learning (2013) — 366 citations
• Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations (2015) — 70 citations
• Optimal kernels for unsupervised learning (2005) — 5 citations
• The optimal unbiased value estimator and its relation to LSTD, TD and MC (2009) — 7 citations
• Fast model-based protein homology detection without alignment (2007) — 136 citations
• A Maximum Common Subgraph Kernel Method for Predicting the Chromosome Aberration Test (2010) — 24 citations
• Construction of approximation spaces for reinforcement learning (2013) — 23 citations
• Sequence classificatio n for protein analysis (2005) — 3 citations
• Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty (2003) — 83 citations
• Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems (2002) — 24 citations