Jeremy Nixon

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #27,475 of 35,345 by trust here, #13,353 by citations
• Optimization & Training — 24 citations into it; #2,209 of 2,601 by trust here, #2,052 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it
• Model Architectures — 9 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Jeremy Nixon trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Kaiming He
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Ronald J. Williams
7. Jia Deng
8. Yann LeCun
9. Ilya Sutskever
10. Alex Graves

Who trusts Jeremy Nixon overall

1. Ananya Kumar
2. Stephanie C. Lin
3. Arsenii Ashukha
4. Mengya Xu
5. Anastasios Nikolas Angelopoulos
6. Zhipeng Ding
7. Joshua Meier
8. Liyang Jiang
9. Luke Metz
10. Percy Liang

Papers from Jeremy Nixon's perspective

Ordered by how strongly the researchers Jeremy Nixon 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
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

• Understanding and correcting pathologies in the training of learned optimizers (2018) — 168 citations
• Analyzing the role of model uncertainty for electronic health records (2019) — 123 citations
• Measuring Calibration in Deep Learning (2019) — 692 citations