Steven N. Evans

Mathematical Physics · 2000–2018

A researcher in the Science corpus: 10 papers; trusts 28 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Mathematical Physics — 33 citations into it; #348 of 8,562 by trust here, #441 by citations
• Geometry and Topology — 21 citations into it
• Statistics and Probability — 18 citations into it; #1,143 of 12,932 by trust here, #1,457 by citations
• Artificial Intelligence — 15 citations into it; #21,894 of 35,345 by trust here, #30,347 by citations
• Computational Theory and Mathematics — 10 citations into it; #7,362 of 12,182 by trust here, #9,306 by citations
• Statistical and Nonlinear Physics — 9 citations into it; #2,189 of 10,127 by trust here, #5,625 by citations
• Applied Mathematics — 8 citations into it
• Discrete Mathematics and Combinatorics — 8 citations into it

Whom Steven N. Evans trusts overall

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

1. Misha Gromov
2. Karl‐Theodor Sturm
3. David J. Aldous
4. Jean-François Le Gall
5. Thomas Duquesne
6. L. Zambotti
7. M. Steel
8. Jim Pitman
9. John Hough
10. Alexander Soshnikov

Who trusts Steven N. Evans overall

1. Bénédicte Haas
2. Jim Pitman
3. Jean-François Le Gall
4. Matthias Winkel
5. Grégory Miermont
6. Thomas Duquesne
7. Persi Diaconis
8. Neil O'Connell
9. Amaury Lambert
10. Bo Chen

Papers from Steven N. Evans's perspective

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

Their papers

• Representing smooth functions as compositions of near-identity functions with implications for deep network optimization (2018) — 34 citations
• Linear functionals of eigenvalues of random matrices (2000) — 262 citations
• Immanants and Finite Point Processes (2000) — 30 citations
• Rayleigh processes, real trees, and root growth with re-grafting (2004) — 158 citations
• SUBTREE PRUNE AND REGRAFT: A REVERSIBLE REAL TREE-VALUED MARKOV PROCESS (2005) — 83 citations
• Hyperdeterminantal point processes (2008) — 6 citations
• Spectra of Large Random Trees (2009) — 53 citations
• Probability and Real Trees (2008) — 182 citations
• Vital Rates from the Action of Mutation Accumulation (2008) — 1,238 citations
• Quasistationary distributions for one-dimensional diffusions with killing (2004) — 67 citations