Sascha Lange

Reinforcement Learning · 2009–2012

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 388 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 47 citations into it; #157 of 2,242 by trust here, #360 by citations
• Artificial Intelligence — 28 citations into it; #944 of 35,345 by trust here, #3,726 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #2,998 of 21,575 by trust here, #9,187 by citations
• Control and Systems Engineering — 15 citations into it; #3,388 of 8,257 by trust here, #1,875 by citations
• Representation & Self-Supervised Learning — 12 citations into it; #575 of 3,145 by trust here, #1,692 by citations
• Optimization & Training — 10 citations into it
• Statistical Learning Theory — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Sascha Lange trusts overall

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

1. Martin A. Riedmiller
2. Geoffrey J. Gordon
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Richard S. Sutton
6. Andrew Barto
7. Damien Ernst
8. Dimitri P. Bertsekas
9. Michail G. Lagoudakis
10. R. Caruana

Who trusts Sascha Lange overall

1. J. Urgen Schmidhuber
2. Sergey Levine
3. Chelsea Finn
4. Lei Tai
5. Yoshua Bengio
6. Pieter Abbeel
7. Amy Zhang
8. Trevor Darrell
9. Ashvin Nair
10. Ruslan Salakhutdinov

Papers from Sascha Lange's perspective

Ordered by how strongly the researchers Sascha Lange 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Batch Reinforcement Learning (2012) — 699 citations
• Deep auto-encoder neural networks in reinforcement learning (2010) — 387 citations
• Autonomous reinforcement learning on raw visual input data in a real world application (2012) — 249 citations
• Reinforcement learning for robot soccer (2009) — 291 citations