Emily Black

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #35,086 of 35,345 by trust here, #26,682 by citations
• Safety, Robustness & Interpretability — 19 citations into it
• AI Ethics, Fairness & Society — 12 citations into it; #490 of 494 by trust here, #396 by citations
• Safety Research — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Emily Black trusts overall

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

1. Cynthia Rudin
2. Jiayun Dong
3. Solon Barocas
4. Alex Krizhevsky
5. Leo Breiman
6. Andrew D. Selbst
7. Pedro M. Domingos
8. Berk Ustun
9. Indrė Žliobaitė
10. Geoffrey E. Hinton

Who trusts Emily Black overall

1. Olga Russakovsky

Papers from Emily Black's perspective

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

Their papers

• Feature-Wise Bias Amplification (2018) — 52 citations
• Model Multiplicity: Opportunities, Concerns, and Solutions (2022) — 174 citations