Marcel Simon

Computer Vision and Pattern Recognition · 2014–2016

A researcher in the Science corpus: 3 papers; trusts 670 researchers and is trusted by 228 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #2,481 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 38 citations into it
• Representation & Self-Supervised Learning — 12 citations into it; #545 of 3,145 by trust here, #1,430 by citations
• Aerospace Engineering — 10 citations into it
• Safety, Robustness & Interpretability — 10 citations into it; #1,006 of 2,251 by trust here, #1,857 by citations
• Model Architectures — 8 citations into it; #1,105 of 2,278 by trust here, #1,826 by citations
• Biophysics — 5 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Marcel Simon trusts overall

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

1. Andrew Zisserman
2. Alex Krizhevsky
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Rob Fergus
6. Ross B. Girshick
7. Ilya Sutskever
8. Kaiming He
9. Christian Szegedy
10. Yann LeCun

Who trusts Marcel Simon overall

1. Quanshi Zhang
2. Heliang Zheng
3. Zhishuai Zhang
4. Erik Rodner
5. Tsung-Yu Lin
6. Song-Chun Zhu
7. Ying Nian Wu
8. Xiangteng He
9. Yuxin Peng
10. Ruiming Cao

Papers from Marcel Simon's perspective

Ordered by how strongly the researchers Marcel Simon 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks (2015) — 426 citations
• Part Detector Discovery in Deep Convolutional Neural Networks (2014) — 76 citations
• Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches (2016) — 41 citations