Leilani H. Gilpin

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 173 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it
• Safety, Robustness & Interpretability — 85 citations into it
• Computer Vision and Pattern Recognition — 69 citations into it
• Natural Language Processing — 14 citations into it
• Biophysics — 13 citations into it
• Model Architectures — 11 citations into it
• AI Ethics, Fairness & Society — 9 citations into it
• Computational Theory and Mathematics — 9 citations into it

Whom Leilani H. Gilpin trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Matthew D. Zeiler
5. Yoshua Bengio
6. Diederik P. Kingma
7. Bolei Zhou
8. Yann LeCun
9. Karen Simonyan
10. Quanshi Zhang

Who trusts Leilani H. Gilpin overall

1. Mark T. Keane
2. Alon Jacovi
3. Amina Adadi
4. Aniek F. Markus
5. Sina Mohseni
6. Hanjie Chen
7. José Jiménez-Luna
8. Nils Rethmeier
9. Umang Bhatt
10. A. Yeung

Papers from Leilani H. Gilpin's perspective

Ordered by how strongly the researchers Leilani H. Gilpin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Explaining Explanations: An Overview of Interpretability of Machine Learning (2018) — 2,256 citations
• Explaining Explanations to Society (2019) — 36 citations
• Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018) — 187 citations