Simon A. A. Kohl

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 235 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 114 citations into it; #14,163 of 21,575 by trust here, #4,887 by citations
• Artificial Intelligence — 91 citations into it
• Model Architectures — 32 citations into it
• Optimization & Training — 23 citations into it
• Biophysics — 12 citations into it; #948 of 1,196 by trust here, #325 by citations
• Neurology — 7 citations into it
• Ocean Engineering — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Simon A. A. Kohl trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. Christian Szegedy
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Simon A. A. Kohl overall

1. Junde Wu
2. Amirhossein Kazerouni
3. Oleh Rybkin
4. Jun Ma
5. Alireza Mehrtash
6. Conghui Hu
7. Dong Wang
8. Jing Zhang
9. Maithra Raghu
10. Ehsan Khodapanah Aghdam

Papers from Simon A. A. Kohl's perspective

Ordered by how strongly the researchers Simon A. A. Kohl trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• A Probabilistic U-Net for Segmentation of Ambiguous Images (2018) — 731 citations
• nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (2020) — 9,406 citations
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation (2018) — 1,100 citations
• Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection (2018) — 233 citations
• Contrastive Training for Improved Out-of-Distribution Detection (2020) — 271 citations
• Automated Design of Deep Learning Methods for Biomedical Image Segmentation (2019) — 325 citations