John C. Snyder

Artificial Intelligence · 2011–2015

A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #19,196 of 35,345 by trust here, #11,192 by citations
• Statistical Learning Theory — 15 citations into it
• Materials Chemistry — 14 citations into it; #3,722 of 11,513 by trust here, #5,258 by citations
• Computational Chemistry & Drug Discovery — 10 citations into it; #203 of 425 by trust here, #204 by citations
• Statistics and Probability — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom John C. Snyder trusts overall

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

1. Fabrizio Sebastiani
2. K. Müller
3. Igor Kononenko
4. Matthias Rupp 
5. Bernhard Schölkopf
6. David Ruppert
7. Trevor J. Hastie
8. Katja Hansen
9. James Franklin
10. A. P. Bart'ok

Who trusts John C. Snyder overall

1. Grgoire Montavon
2. Evgeny V. Podryabinkin
3. Matthias Rupp 
4. Tânia F Cova
5. K. Müller
6. Ghanshyam Pilania
7. Aldo Glielmo
8. Rampi Ramprasad
9. Li Li
10. Kieron Burke

Papers from John C. Snyder's perspective

Ordered by how strongly the researchers John C. Snyder trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. The Nature of Statistical Learning Theory
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Finding Density Functionals with Machine Learning (2011) — 507 citations
• Understanding Machine-learned Density Functionals (2014) — 164 citations
• Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives (2015) — 31 citations