Clint Scovel

Artificial Intelligence · 2005–2015

A researcher in the Science corpus: 9 papers; trusts 37 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #6,106 of 35,345 by trust here, #10,942 by citations
• Statistical Learning Theory — 37 citations into it; #310 of 1,038 by trust here, #417 by citations
• Statistics and Probability — 26 citations into it; #2,174 of 12,932 by trust here, #3,330 by citations
• Mathematical Physics — 18 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #15,464 of 21,575 by trust here, #13,269 by citations
• Applied Mathematics — 13 citations into it; #3,666 of 7,620 by trust here, #3,258 by citations
• Control and Systems Engineering — 11 citations into it; #3,342 of 8,257 by trust here, #4,045 by citations
• Computational Theory and Mathematics — 10 citations into it

Whom Clint Scovel trusts overall

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

1. Ingo Steinwart
2. Bernhard Schölkopf
3. Steve Smale
4. Andreas Christmann
5. Ding-Xuan Zhou
6. Felipe Cucker
7. Alain F. Berlinet
8. Charles A. Micchelli
9. Alex Smola
10. John Shawe-Taylor

Who trusts Clint Scovel overall

1. Ingo Steinwart
2. Francis R. Bach
3. Ying‐Qi Zhao
4. Lorenzo Rosasco
5. Francesco Orabona
6. Régis Vert
7. Don R. Hush
8. Alessandro Rudi
9. Kacper P. Chwialkowski
10. Peter L. Bartlett

Papers from Clint Scovel's perspective

Ordered by how strongly the researchers Clint Scovel 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Fast rates for support vector machines using Gaussian kernels (2007) — 293 citations
• Radial kernels and their reproducing kernel Hilbert spaces (2010) — 40 citations
• Separability of reproducing kernel spaces (2015) — 47 citations
• Conditioning Gaussian measure on Hilbert space (2015) — 49 citations
• Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs (2012) — 271 citations
• Optimal Rates for Regularized Least Squares Regression (2009) — 332 citations
• An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels (2006) — 290 citations
• QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines (2006) — 88 citations
• A Classification Framework for Anomaly Detection (2005) — 359 citations