George C. Runger

Artificial Intelligence · 2003–2014

A researcher in the Science corpus: 12 papers; trusts 76 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #5,117 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #4,935 of 21,575 by trust here, #11,950 by citations
• Signal Processing — 24 citations into it; #2,604 of 7,572 by trust here, #3,428 by citations
• Statistics and Probability — 22 citations into it; #9,709 of 12,932 by trust here, #7,914 by citations
• Information Systems — 16 citations into it; #7,619 of 8,771 by trust here, #8,676 by citations
• Molecular Biology — 16 citations into it; #2,826 of 17,773 by trust here, #13,563 by citations
• Evaluation & Benchmarks — 15 citations into it
• Statistical Learning Theory — 15 citations into it

Whom George C. Runger trusts overall

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

1. Isabelle M Guyon
2. Jerome H. Friedman
3. Eamonn J. Keogh
4. Leo Breiman
5. Trevor J. Hastie
6. Robert Tibshirani
7. Chotirat (Ann) Ratanamahatana
8. J. Ross Quinlan
9. Yoav Freund
10. Niko Speybroeck

Who trusts George C. Runger overall

1. Houtao Deng
2. Miron Bartosz Kursa
3. William H. Woodall
4. Anthony J. Bagnall
5. Jason Lines
6. Emilio Carrizosa
7. Gary Smith
8. Gérard Biau
9. Rafael Blanquero
10. Cristina Molero-Río

Papers from George C. Runger's perspective

Ordered by how strongly the researchers George C. Runger 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Regression Shrinkage and Selection via the Lasso
10. Going deeper with convolutions

Their papers

• A time series forest for classification and feature extraction (2013) — 622 citations
• Multivariate Extensions to Cumulative Sum Control Charts (2004) — 60 citations
• Process monitoring for multiple count data using generalized linear model-based control charts (2003) — 135 citations
• Gene selection with guided regularized random forest (2012) — 304 citations
• Robustness properties of multivariate EWMA control charts (2003) — 80 citations
• CBC: An associative classifier with a small number of rules (2014) — 40 citations
• A Bag-of-Features Framework to Classify Time Series (2013) — 363 citations
• Feature selection via regularized trees (2012) — 227 citations
• Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination (2009) — 346 citations
• Process monitoring for mean shifts for multiple stage processes (2007) — 60 citations
• Model-based process monitoring using robust generalized linear models (2005) — 58 citations
• Generalized Linear Model‐based Control Charts for Discrete Semiconductor Process Data (2004) — 59 citations