Jan Hendrik Metzen

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 8 papers; trusts 645 researchers and is trusted by 125 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 153 citations into it; #2,198 of 35,345 by trust here, #2,839 by citations
• Computer Vision and Pattern Recognition — 111 citations into it; #4,945 of 21,575 by trust here, #6,873 by citations
• Model Architectures — 48 citations into it
• Safety, Robustness & Interpretability — 47 citations into it; #312 of 2,251 by trust here, #335 by citations
• Optimization & Training — 38 citations into it
• Neural Architecture Search & AutoML — 37 citations into it; #25 of 283 by trust here, #39 by citations
• Efficiency & Compression — 17 citations into it
• Reinforcement Learning — 12 citations into it

Whom Jan Hendrik Metzen trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Ian J. Goodfellow
5. Yoshua Bengio
6. Diederik P. Kingma
7. Karen Simonyan
8. Christian Szegedy
9. Ilya Sutskever
10. Yann LeCun

Who trusts Jan Hendrik Metzen overall

1. Nicholas Carlini
2. Eric Wong
3. Haifeng Jin
4. Dan Hendrycks
5. Tianyu Pang
6. Yinpeng Dong
7. Mahmood Sharif
8. Edward Chou
9. Jiajun Lu
10. Qingquan Song

Papers from Jan Hendrik Metzen's perspective

Ordered by how strongly the researchers Jan Hendrik Metzen 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Simple And Efficient Architecture Search for Convolutional Neural Networks (2017) — 245 citations
• On Detecting Adversarial Perturbations (2017) — 1,037 citations
• Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution (2018) — 546 citations
• Universal Adversarial Perturbations Against Semantic Image Segmentation (2017) — 309 citations
• Scaling provable adversarial defenses (2018) — 474 citations
• Adversarial Examples for Semantic Image Segmentation (2017) — 123 citations
• Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions (2016) — 215 citations
• Multi-objective Architecture Search for CNNs (2018) — 59 citations