Jeffrey De Fauw

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 618 researchers and is trusted by 952 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 214 citations into it; #1,415 of 21,575 by trust here, #1,669 by citations
• Artificial Intelligence — 159 citations into it; #8,681 of 35,345 by trust here, #2,401 by citations
• Representation & Self-Supervised Learning — 112 citations into it; #1,534 of 3,145 by trust here, #409 by citations
• Generative Models — 50 citations into it; #1,494 of 1,956 by trust here, #1,635 by citations
• Signal Processing — 39 citations into it
• Multimodal Learning — 35 citations into it
• Model Architectures — 33 citations into it
• Evaluation & Benchmarks — 29 citations into it; #1,576 of 3,147 by trust here, #574 by citations

Whom Jeffrey De Fauw trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Yoshua Bengio
5. Alex Krizhevsky
6. Aäron van den Oord
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. Oriol Vinyals

Who trusts Jeffrey De Fauw overall

1. Ali Razavi
2. Taco Cohen
3. Aäron van den Oord
4. Sander Dieleman
5. Kaiming He
6. Erik J. Bekkers
7. David W. Romero
8. Oriol Vinyals
9. Xinlei Chen
10. Ishan Misra

Papers from Jeffrey De Fauw's perspective

Ordered by how strongly the researchers Jeffrey De Fauw 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. 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Data-Efficient Image Recognition with Contrastive Predictive Coding (2019) — 1,574 citations
• Self-Supervised MultiModal Versatile Networks (2020) — 416 citations
• Exploiting Cyclic Symmetry in Convolutional Neural Networks (2016) — 381 citations
• Hierarchical Autoregressive Image Models with Auxiliary Decoders (2019) — 41 citations
• A Probabilistic U-Net for Segmentation of Ambiguous Images (2018) — 731 citations
• International evaluation of an AI system for breast cancer screening (2020) — 2,721 citations
• Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy (2018) — 337 citations
• Lasagne: First release. (2015)