Niklas Wahlstrom

Reinforcement Learning · 2014–2015

A researcher in the Science corpus: 3 papers; trusts 68 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 37 citations into it; #757 of 2,242 by trust here, #1,227 by citations
• Artificial Intelligence — 31 citations into it; #18,172 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #6,377 of 21,575 by trust here, #9,305 by citations
• Control and Systems Engineering — 16 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Robotics & Control — 11 citations into it
• Optimization & Training — 8 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Niklas Wahlstrom trusts overall

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

1. Geoffrey E. Hinton
2. Marc Peter Deisenroth
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Alex Krizhevsky
6. Jeff Schneider
7. Stefan Schaal
8. Yann LeCun
9. Richard S. Sutton
10. Jorge Nocedal

Who trusts Niklas Wahlstrom overall

1. Marc Peter Deisenroth
2. Kai Arulkumaran
3. Kamyar Azizzadenesheli
4. Sergey Levine
5. Felix Leibfried
6. Thomas Bo Schön
7. David R Ha
8. Arunkumar Byravan
9. David Balduzzi
10. Herke Van Hoof

Papers from Niklas Wahlstrom's perspective

Ordered by how strongly the researchers Niklas Wahlstrom 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015) — 192 citations
• Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models (2015) — 39 citations
• Learning deep dynamical models from image pixels (2014) — 67 citations