Felix Leibfried

Reinforcement Learning · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 134 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 73 citations into it
• Artificial Intelligence — 62 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Optimization & Training — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Control and Systems Engineering — 9 citations into it
• Generative Models — 9 citations into it

Whom Felix Leibfried trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Richard S. Sutton
5. Alex Krizhevsky
6. Xavier Glorot
7. Jimmy Ba
8. Ben J. A. Kröse
9. Yann LeCun
10. Ilya Sutskever

Who trusts Felix Leibfried overall

1. Lukasz Kaiser
2. Kacper Kielak
3. Sébastien Racanière
4. Ramitha Sundar
5. Mohammad Babaeizadeh

Papers from Felix Leibfried's perspective

Ordered by how strongly the researchers Felix Leibfried 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games (2016) — 45 citations
• Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks (2018) — 7 citations
• Model-Based Stabilisation of Deep Reinforcement Learning (2018)
• Soft Q-Learning with Mutual-Information Regularization (2018) — 68 citations