Dan Busbridge

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 90 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #24,710 of 35,345 by trust here, #26,682 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #15,280 of 21,575 by trust here, #17,757 by citations
• Model Architectures — 23 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 16 citations into it
• Materials Chemistry — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Dan Busbridge trusts overall

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

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

Who trusts Dan Busbridge overall

1. Katsuhiko Ishiguro
2. Shaked Brody
3. Shin-ichi Maeda
4. David Bieber
5. Masanori Koyama
6. Uri Alon
7. Charles Sutton

Papers from Dan Busbridge's perspective

Ordered by how strongly the researchers Dan Busbridge 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. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Relational Graph Attention Networks (2018) — 247 citations
• Stabilizing Transformer Training by Preventing Attention Entropy Collapse (2023) — 184 citations
• Do Self-Supervised and Supervised Methods Learn Similar Visual Representations? (2021) — 31 citations
• Learning medical triage from clinicians using Deep Q-Learning (2020) — 4 citations