Katherine L. Hermann

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 356 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #31,573 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 75 citations into it; #19,408 of 21,575 by trust here, #16,787 by citations
• Safety, Robustness & Interpretability — 36 citations into it
• Cognitive Neuroscience — 21 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Optimization & Training — 15 citations into it
• Scaling & Learning Theory — 10 citations into it
• Model Architectures — 8 citations into it

Whom Katherine L. Hermann trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Jeff Donahue
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Katherine L. Hermann overall

1. Polina Kirichenko
2. Andrew Kyle Lampinen
3. Robert Geirhos
4. Pavel Izmailov
5. Andrew Gordon Wilson
6. Md. Amirul Islam
7. Nicholas A. Roy
8. Thao Nguyen
9. James L. McClelland
10. Kantharaju Narayanappa

Papers from Katherine L. Hermann's perspective

Ordered by how strongly the researchers Katherine L. Hermann 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• What shapes feature representations? Exploring datasets, architectures, and training (2020) — 179 citations
• The Origins and Prevalence of Texture Bias in Convolutional Neural Networks (2019) — 356 citations