Simon Jenni

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 845 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 96 citations into it; #8,525 of 21,575 by trust here, #12,172 by citations
• Artificial Intelligence — 62 citations into it; #12,985 of 35,345 by trust here, #18,186 by citations
• Representation & Self-Supervised Learning — 52 citations into it; #1,458 of 3,145 by trust here, #2,148 by citations
• Generative Models — 19 citations into it
• Optimization & Training — 14 citations into it
• Signal Processing — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Model Architectures — 6 citations into it

Whom Simon Jenni trusts overall

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

1. Ross B. Girshick
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Diederik P. Kingma
6. Nitish Srivastava
7. Jeff Donahue
8. Yoshua Bengio
9. Sergey Ioffe
10. Ilya Sutskever

Who trusts Simon Jenni overall

1. Chengxu Zhuang
2. Zeyu Feng
3. Sungnyun Kim
4. Yi Wang
5. Mathilde Caron
6. Chang Xu
7. Hailin Jin
8. Alex Zhai
9. Gihun Lee
10. Conrad M. Albrecht

Papers from Simon Jenni's perspective

Ordered by how strongly the researchers Simon Jenni 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Self-Supervised Feature Learning by Learning to Spot Artifacts (2018) — 133 citations
• Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics (2020) — 49 citations