Kevin Eykholt

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 5 papers; trusts 43 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #23,601 of 35,345 by trust here, #4,772 by citations
• Safety, Robustness & Interpretability — 85 citations into it; #1,761 of 2,251 by trust here, #288 by citations
• Computer Vision and Pattern Recognition — 72 citations into it
• Signal Processing — 19 citations into it; #5,838 of 7,572 by trust here, #812 by citations

Whom Kevin Eykholt trusts overall

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

1. Ian J. Goodfellow
2. Christian Szegedy
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Nicolas Papernot
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Nicholas Carlini
10. Yoshua Bengio

Who trusts Kevin Eykholt overall

1. Stepan Alekseevich Komkov
2. Florian Tramèr
3. Aleksandr Petiushko
4. Edward Chou
5. Yulong Cao
6. Ivan Evtimov
7. Chaowei Xiao
8. Simen Thys
9. Bo Li
10. Jiajun Lu

Papers from Kevin Eykholt's perspective

Ordered by how strongly the researchers Kevin Eykholt 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Robust Physical-World Attacks on Deep Learning Visual Classification (2018) — 2,198 citations
• Robust Physical-World Attacks on Deep Learning Models (2017) — 621 citations
• Note on Attacking Object Detectors with Adversarial Stickers (2017) — 37 citations
• Physical Adversarial Examples for Object Detectors (2018) — 554 citations
• Robust Physical-World Attacks on Machine Learning Models (2017) — 343 citations