Matthew Faulkner

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #35,113 of 35,345 by trust here, #30,347 by citations
• Signal Processing — 13 citations into it
• Computational Mechanics — 9 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Computational Mathematics — 5 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Matthew Faulkner trusts overall

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

1. Dan Feldman
2. Muthu Dayalan
3. Sariel Har-Peled
4. Olivier Bachem
5. Michael Langberg
6. David Haussler
7. Sanjeev Arora
8. Arthur P. Dempster
9. David Arthur
10. Radford M. Neal

Who trusts Matthew Faulkner overall

1. Dan Feldman
2. Olivier Bachem
3. Mario Lucic
4. Andreas Krause
5. Artem Barger
6. David Lopez-Paz
7. Jonathan Huggins
8. Trevor Campbell
9. Zalán Borsos
10. Thomas Hofmann

Papers from Matthew Faulkner's perspective

Ordered by how strongly the researchers Matthew Faulkner 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. ImageNet Large Scale Visual Recognition Challenge
9. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Training Gaussian Mixture Models at Scale via Coresets (2017) — 114 citations
• Training Mixture Models at Scale via Coresets (2017) — 30 citations
• Scalable Training of Mixture Models via Coresets (2011) — 170 citations