Jette Henderson

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #19,555 of 35,345 by trust here, #24,417 by citations
• Safety, Robustness & Interpretability — 30 citations into it; #945 of 2,251 by trust here, #1,880 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Optimization & Training — 12 citations into it
• Information Systems — 11 citations into it
• Privacy & Security — 9 citations into it
• Safety Research — 8 citations into it
• AI Ethics, Fairness & Society — 6 citations into it; #160 of 494 by trust here, #338 by citations

Whom Jette Henderson trusts overall

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

1. Frank McSherry
2. Zhou Wang
3. Yehuda Koren
4. Chris Russell
5. Ron Kohavi
6. Reuben Binns
7. Sandra Wachter
8. Berk Ustun
9. Riccardo Guidotti
10. Christian Szegedy

Who trusts Jette Henderson overall

1. Susanne Dandl
2. Amir-Hossein Karimi
3. Sahil Verma
4. Christoph Molnar
5. Bernhard Schölkopf
6. Anna Markella Antoniadi

Papers from Jette Henderson's perspective

Ordered by how strongly the researchers Jette Henderson 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. ImageNet: A large-scale hierarchical image database
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Et al
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Federating Recommendations Using Differentially Private Prototypes (2020) — 47 citations
• CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models (2019) — 192 citations
• CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (2019) — 102 citations