Edward Snelson

Artificial Intelligence · 2003–2012

A researcher in the Science corpus: 6 papers; trusts 23 researchers and is trusted by 186 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #13,625 of 35,345 by trust here, #5,924 by citations
• Probabilistic & Bayesian Methods — 20 citations into it; #100 of 1,525 by trust here, #200 by citations
• Management Science and Operations Research — 17 citations into it; #7,223 of 11,946 by trust here, #670 by citations
• Statistics and Probability — 17 citations into it
• Reinforcement Learning — 12 citations into it; #1,578 of 2,242 by trust here, #591 by citations
• Control and Systems Engineering — 8 citations into it; #3,421 of 8,257 by trust here, #6,083 by citations
• Optimization & Training — 8 citations into it
• Statistical Learning Theory — 7 citations into it; #87 of 1,038 by trust here, #148 by citations

Whom Edward Snelson trusts overall

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

1. Matthias W. Seeger
2. Lehel Csató
3. Christopher K. I. Williams
4. Volker Tresp
5. Tom Minka
6. Neil D. Lawrence
7. Alex Smola
8. Iain Murray
9. Manfred Opper
10. Zoubin Ghahramani

Who trusts Edward Snelson overall

1. Marc Peter Deisenroth
2. Carl E. Rasmussen
3. Michalis K. Titsias
4. Zoubin Ghahramani
5. Neil D. Lawrence
6. Andrew Gordon Wilson
7. Jan Peters
8. Mauricio A Álvarez
9. David Kristjanson Duvenaud
10. José Miguel Hernández-Lobato

Papers from Edward Snelson's perspective

Ordered by how strongly the researchers Edward Snelson 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. Learning Multiple Layers of Features from Tiny Images
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Counterfactual reasoning and learning systems: the example of computational advertising (2012) — 859 citations
• Variable Noise and Dimensionality Reduction for Sparse Gaussian processes (2006) — 84 citations
• Bayesian inference for Plackett-Luce ranking models (2009) — 207 citations
• Local and global sparse Gaussian process approximations (2007) — 308 citations
• Sparse Gaussian Processes using Pseudo-inputs (2005) — 2,039 citations
• Warped Gaussian Processes (2003) — 390 citations