Christian Osendorfer

Artificial Intelligence · 2008–2018

A researcher in the Science corpus: 7 papers; trusts 278 researchers and is trusted by 102 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #2,593 of 35,345 by trust here, #6,378 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #14,730 of 21,575 by trust here, #15,955 by citations
• Model Architectures — 45 citations into it; #394 of 2,278 by trust here, #1,215 by citations
• Optimization & Training — 31 citations into it; #2,050 of 2,601 by trust here, #1,385 by citations
• Natural Language Processing — 17 citations into it
• Reinforcement Learning — 17 citations into it; #1,714 of 2,242 by trust here, #1,344 by citations
• Signal Processing — 15 citations into it
• Generative Models — 14 citations into it; #325 of 1,956 by trust here, #1,010 by citations

Whom Christian Osendorfer trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Koray Kavukcuoglu
9. Razvan Pascanu
10. J. Urgen Schmidhuber

Who trusts Christian Osendorfer overall

1. J. Urgen Schmidhuber
2. Yoshua Bengio
3. Jost Tobias Springenberg
4. Yarin Gal
5. Florian Schmidt
6. Ruslan Salakhutdinov
7. David R Ha
8. Marc Peter Deisenroth
9. Zhe Gan
10. Sergey Levine

Papers from Christian Osendorfer's perspective

Ordered by how strongly the researchers Christian Osendorfer 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning Stochastic Recurrent Networks (2014) — 292 citations
• Parameter-exploring policy gradients (2010) — 301 citations
• On Fast Dropout and its Applicability to Recurrent Networks (2013) — 66 citations
• Image Super-Resolution with Fast Approximate Convolutional Sparse Coding (2014) — 81 citations
• Policy Gradients with Parameter-Based Exploration for Control (2008) — 83 citations
• NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations (2018) — 59 citations
• Variational inference of latent state sequences using Recurrent Networks (2014)