Jacob R. Kauffmann

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 37 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 214 citations into it
• Computer Vision and Pattern Recognition — 89 citations into it
• Representation & Self-Supervised Learning — 44 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Statistics and Probability — 27 citations into it
• Molecular Biology — 17 citations into it
• Control and Systems Engineering — 13 citations into it
• Model Architectures — 11 citations into it

Whom Jacob R. Kauffmann trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Radford M. Neal
9. Max Welling
10. David John Cameron MacKay

Papers from Jacob R. Kauffmann's perspective

Ordered by how strongly the researchers Jacob R. Kauffmann 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Unifying Review of Deep and Shallow Anomaly Detection (2020) — 1,067 citations