Felix Leeb

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 115 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 43 citations into it; #18,278 of 21,575 by trust here, #14,478 by citations
• Artificial Intelligence — 31 citations into it
• Reinforcement Learning — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Aerospace Engineering — 12 citations into it
• Control and Systems Engineering — 12 citations into it
• Generative Models — 8 citations into it
• Causal Inference & Discovery — 5 citations into it

Whom Felix Leeb trusts overall

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

1. Bernhard Schölkopf
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Alex Krizhevsky
7. Chelsea Finn
8. Yann LeCun
9. Sergey Levine
10. J. Urgen Schmidhuber

Who trusts Felix Leeb overall

1. Mohak Bhardwaj
2. Bernhard Schölkopf
3. Frederik D. Ebert
4. Mengyuan Yan

Papers from Felix Leeb's perspective

Ordered by how strongly the researchers Felix Leeb 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control (2017) — 43 citations
• SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Control (2018) — 37 citations
• Structural Autoencoders Improve Representations for Generation and Transfer (2020) — 13 citations