Peter König

Computer Vision and Pattern Recognition · 2000–2018

A researcher in the Science corpus: 10 papers; trusts 370 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #8,748 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 62 citations into it; #13,373 of 35,345 by trust here, #21,316 by citations
• Cognitive Neuroscience — 47 citations into it; #3,174 of 10,852 by trust here, #2,195 by citations
• Representation & Self-Supervised Learning — 24 citations into it; #1,890 of 3,145 by trust here, #2,691 by citations
• Optimization & Training — 23 citations into it
• Model Architectures — 20 citations into it
• Neuroscience — 13 citations into it
• Experimental and Cognitive Psychology — 10 citations into it; #5,808 of 9,731 by trust here, #7,727 by citations

Whom Peter König trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Xavier Glorot
8. X. Zhang
9. Diederik P. Kingma
10. Ian J. Goodfellow

Who trusts Peter König overall

1. Raphael Gontijo Lopes
2. Yi-Chuan Chen
3. Alex Hernandez-Garcia
4. Hamed Ghoddusi
5. Sylvia J. Smullin
6. Sungbin Lim
7. Ekin Dogus Cubuk
8. Charles Spence
9. Shuxiao Chen
10. J. Matias Palva

Papers from Peter König's perspective

Ordered by how strongly the researchers Peter König 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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Data augmentation instead of explicit regularization (2018) — 166 citations
• Learning viewpoint invariant object representations using a temporal coherence principle (2005) — 63 citations
• Extracting Slow Subspaces from Natural Videos Leads to Complex Cells (2001) — 77 citations
• Further Advantages of Data Augmentation on Convolutional Neural Networks (2018) — 126 citations
• Attentional Resource Allocation in Visuotactile Processing Depends on the Task, But Optimal Visuotactile Integration Does Not Depend on Attentional Resources (2016) — 34 citations
• Faculty Opinions recommendation of PSYCHOLOGY. Estimating the reproducibility of psychological science. (2015) — 246 citations
• Primary Visual Cortex Represents the Difference Between Past and Present (2013) — 33 citations
• Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data (2012)
• Gaze allocation in natural stimuli: Comparing free exploration to head-fixed viewing conditions (2009) — 105 citations
• Top-down processing mediated by interareal synchronization. (2000) — 763 citations