Anton Maximilian Schäfer

Model Architectures · 2005–2008

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Model Architectures — 10 citations into it; #1,083 of 2,278 by trust here, #1,726 by citations
• Artificial Intelligence — 6 citations into it; #13,228 of 35,345 by trust here, #18,186 by citations
• Reinforcement Learning — 6 citations into it

Whom Anton Maximilian Schäfer trusts overall

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

1. George V. Cybenko
2. Ken-ichi Funahashi
3. David E. Rumelhart
4. Sepp Hochreiter
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Simon S. Haykin
8. Ronald J. Williams
9. Richard S. Sutton
10. Martin A. Riedmiller

Who trusts Anton Maximilian Schäfer overall

1. Daniel Hein
2. J. Urgen Schmidhuber
3. Nikolaus Kriegeskorte
4. Hans-Georg Zimmermann
5. Hansika Hewamalage
6. Steffen Udluft
7. Rico Jonschkowski
8. Hojjat Adeli
9. Christoph Bergmeir
10. Thomas A. Runkler

Papers from Anton Maximilian Schäfer's perspective

Ordered by how strongly the researchers Anton Maximilian Schäfer 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Reinforcement Learning: An Introduction
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Recurrent Neural Networks Are Universal Approximators (2006) — 480 citations
• Learning Long Term Dependencies with Recurrent Neural Networks (2006) — 64 citations
• Safe exploration for reinforcement learning (2008) — 146 citations
• SOLVING PARTIALLY OBSERVABLE REINFORCEMENT LEARNING PROBLEMS WITH RECURRENT NEURAL NETWORKS (2005)