David Matheson

Artificial Intelligence · 2013

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #8,972 of 35,345 by trust here, #12,244 by citations
• Computational Theory and Mathematics — 20 citations into it; #9,229 of 12,182 by trust here, #5,465 by citations
• Management Science and Operations Research — 19 citations into it
• Optimization & Training — 18 citations into it
• Statistics and Probability — 17 citations into it
• Statistical Learning Theory — 14 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Computer Networks and Communications — 8 citations into it

Whom David Matheson trusts overall

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

1. Leo Breiman
2. Gérard Biau
3. Frank Hutter
4. Luc Devroye
5. Yi Lin
6. Trevor J. Hastie
7. Nicolai Meinshausen
8. Niko Speybroeck
9. Jamie Shotton
10. Chih-Jen Lin

Who trusts David Matheson overall

1. Gilles Louppe
2. Stefan Wager
3. Erwan Scornet
4. Peter I. Frazier
5. Ruben Martinez-Cantin
6. Susan Athey
7. Alexander Kirillov
8. Balaji Lakshminarayanan
9. András A. Benczúr
10. Ziyun Wang

Papers from David Matheson's perspective

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

Their papers

• Consistency of Online Random Forests (2013) — 93 citations
• Narrowing the Gap: Random Forests In Theory and In Practice (2013) — 244 citations
• Bayesian Optimization in a Billion Dimensions via Random Embeddings (2013) — 551 citations
• UvA-DARE (Digital Academic Repository) Bayesian Optimization in High Dimensions via Random Embeddings (2013)