Daniel D. Johnson

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 2 papers; trusts 126 researchers and is trusted by 78 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #33,227 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #3,270 of 21,575 by trust here, #6,371 by citations
• Generative Models — 30 citations into it; #1,866 of 1,956 by trust here, #891 by citations
• Natural Language Processing — 19 citations into it
• Model Architectures — 13 citations into it; #2,113 of 2,278 by trust here, #1,092 by citations
• Probabilistic & Bayesian Methods — 12 citations into it
• Signal Processing — 10 citations into it; #7,168 of 7,572 by trust here, #1,485 by citations
• Graph Learning — 9 citations into it

Whom Daniel D. Johnson trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Oriol Vinyals
6. Jason Aaron Edward Weston
7. Alex Graves
8. Yann LeCun
9. Ilya Sutskever
10. Kaiming He

Who trusts Daniel D. Johnson overall

1. Jiaxuan You
2. De-An Huang
3. Miltiadis Allamanis
4. Nicola De Cao
5. Yibo Li
6. Qi Liu
7. Luca Franceschi
8. Rex Ying
9. Yujia Li
10. Thomas Kipf

Papers from Daniel D. Johnson's perspective

Ordered by how strongly the researchers Daniel D. Johnson 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Structured Denoising Diffusion Models in Discrete State-Spaces (2021) — 1,984 citations
• Learning Graphical State Transitions (2016) — 98 citations