Heiko Wersing

Artificial Intelligence · 2003–2018

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #9,295 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #7,506 of 21,575 by trust here, #15,162 by citations
• Optimization & Training — 10 citations into it
• Signal Processing — 9 citations into it
• Statistical Learning Theory — 7 citations into it; #798 of 1,038 by trust here, #920 by citations
• Management Science and Operations Research — 6 citations into it; #6,758 of 11,946 by trust here, #8,469 by citations
• Neuroscience — 5 citations into it

Whom Heiko Wersing trusts overall

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

1. Sven Behnke
2. Maximilian Riesenhuber
3. Aapo Hyvärinen
4. Barbara Olshausen
5. Tomaso A. Poggio
6. Anthony J. Bell
7. William T. Freeman
8. Daniel D. Lee
9. Terrence J. Sejnowski
10. Patrik O. Hoyer

Who trusts Heiko Wersing overall

1. Will Y. Zou
2. Dileep George
3. Thomas Serre
4. Alexander Andreopoulos
5. Hossein Mobahi
6. John K. Tsotsos
7. Jeff Hawkins
8. Ronan Collobert
9. Jason Aaron Edward Weston
10. Gabriel Kreiman

Papers from Heiko Wersing's perspective

Ordered by how strongly the researchers Heiko Wersing 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Optimized Features for Hierarchical Models of Invariant Object Recognition (2003) — 196 citations
• Incremental on-line learning: A review and comparison of state of the art algorithms (2018) — 353 citations
• Mitigating Concept Drift via Rejection (2018) — 16 citations