Nathan Fenner

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #15,764 of 35,345 by trust here, #15,542 by citations
• Probabilistic & Bayesian Methods — 7 citations into it
• Atmospheric Science — 5 citations into it
• Global and Planetary Change — 5 citations into it

Whom Nathan Fenner trusts overall

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

1. Yarin Gal
2. David John Cameron MacKay
3. J. Platt
4. Zoubin Ghahramani
5. Alex Kendall
6. Geoffrey E. Hinton
7. Alexandru Niculescu-Mizil
8. Yoshua Bengio
9. Radford M. Neal
10. Diederik P. Kingma

Who trusts Nathan Fenner overall

1. Ananya Kumar
2. Colin White
3. Soumya Ghosh
4. Di Feng
5. Alexander Amini
6. Laurent Valentin Jospin
7. Wesley J. Maddox
8. Jeremy Nixon
9. D. Roman
10. Erik A. Daxberger

Papers from Nathan Fenner's perspective

Ordered by how strongly the researchers Nathan Fenner 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Accurate Uncertainties for Deep Learning Using Calibrated Regression (2018) — 863 citations