F. D. Fave

Probabilistic & Bayesian Methods · 2016

A researcher in the Science corpus: 1 paper; trusts 27 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 16 citations into it; #1,117 of 1,525 by trust here, #1,274 by citations
• Artificial Intelligence — 15 citations into it; #21,665 of 35,345 by trust here, #28,481 by citations
• Optimization & Training — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom F. D. Fave trusts overall

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

1. Tom Minka
2. Radford M. Neal
3. Geoffrey E. Hinton
4. Max Welling
5. Alex Krizhevsky
6. David M. Blei
7. Diederik P. Kingma
8. Ilya Sutskever
9. Vinod Nair
10. Alex Graves

Who trusts F. D. Fave overall

1. Hippolyt Ritter
2. Antonio Loquercio
3. Anqi Wu

Papers from F. D. Fave's perspective

Ordered by how strongly the researchers F. D. Fave 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Assumed Density Filtering Methods for Learning Bayesian Neural Networks (2016) — 56 citations