Christopher Fifty

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 466 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 315 citations into it; #16,202 of 35,345 by trust here, #7,112 by citations
• Computer Vision and Pattern Recognition — 181 citations into it
• Representation & Self-Supervised Learning — 73 citations into it
• Optimization & Training — 51 citations into it
• Computational Mechanics — 26 citations into it
• Natural Language Processing — 26 citations into it
• Graph Learning — 25 citations into it; #348 of 693 by trust here, #188 by citations
• Model Architectures — 22 citations into it; #1,255 of 2,278 by trust here, #694 by citations

Whom Christopher Fifty trusts overall

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

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

Who trusts Christopher Fifty overall

1. Zhiqi Li
2. Vamsi Krishna Aribandi
3. Matthias Fey
4. William L. Hamilton
5. Junliang Yu

Papers from Christopher Fifty's perspective

Ordered by how strongly the researchers Christopher Fifty 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Simplifying Graph Convolutional Networks (2019) — 3,924 citations
• Efficiently Identifying Task Groupings for Multi-Task Learning (2021) — 332 citations
• Co-training Transformer with Videos and Images Improves Action Recognition (2021) — 63 citations
• Multi-task Learning (2020)
• Small Towers Make Big Differences (2020) — 11 citations