Phillip E. Pope

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 312 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #17,005 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 78 citations into it; #20,831 of 21,575 by trust here, #21,103 by citations
• Safety, Robustness & Interpretability — 19 citations into it; #1,216 of 2,251 by trust here, #1,349 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 14 citations into it
• Graph Learning — 11 citations into it
• Natural Language Processing — 8 citations into it
• Computational Mechanics — 7 citations into it

Whom Phillip E. Pope trusts overall

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

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

Who trusts Phillip E. Pope overall

1. Hao Yuan
2. Weiwei Jiang
3. Pouya Pezeshkpour
4. Xianglin Liu
5. D. V. Carvalho
6. Josh Payne
7. Haiyang Yu
8. Karthikeyan K
9. Dongsheng Luo
10. Jiayun Luo

Papers from Phillip E. Pope's perspective

Ordered by how strongly the researchers Phillip E. Pope 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Influence Functions in Deep Learning Are Fragile (2020) — 327 citations
• Explainability Methods for Graph Convolutional Neural Networks (2019) — 586 citations
• The Intrinsic Dimension of Images and Its Impact on Learning (2021) — 413 citations
• Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment (2019) — 24 citations