Gavin C. Cawley

Artificial Intelligence · 2003–2015

A researcher in the Science corpus: 9 papers; trusts 53 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #4,271 of 35,345 by trust here, #6,330 by citations
• Statistical Learning Theory — 31 citations into it; #248 of 1,038 by trust here, #307 by citations
• Optimization & Training — 28 citations into it
• Statistics and Probability — 27 citations into it; #6,951 of 12,932 by trust here, #5,661 by citations
• Evaluation & Benchmarks — 20 citations into it; #563 of 3,147 by trust here, #1,431 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #16,042 of 21,575 by trust here, #15,955 by citations
• Probabilistic & Bayesian Methods — 11 citations into it
• Model Architectures — 8 citations into it

Whom Gavin C. Cawley trusts overall

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

1. Vladimir Naumovich Vapnik
2. Christopher K. I. Williams
3. David John Cameron MacKay
4. Jaka Demsar
5. Gunnar Rätsch
6. Ron Kohavi
7. Bernhard Schölkopf
8. Grace Wahba
9. Yuhai Wu
10. Corinna Cortes

Who trusts Gavin C. Cawley overall

1. Jakramate Bootkrajang
2. Isabelle M Guyon
3. Nicola L. C. Talbot
4. Aki Vehtari
5. Mohammad Mahdi Bejani
6. Ata Kabán
7. Juho Piironen
8. Ahmed M. Alaa
9. Ilja Kuzborskij
10. Russell D. Wolfinger

Papers from Gavin C. Cawley's perspective

Ordered by how strongly the researchers Gavin C. Cawley 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. 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. 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

• On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation (2010) — 2,364 citations
• Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge (2008) — 34 citations
• Model Selection: Beyond the Bayesian/Frequentist Divide (2010) — 183 citations
• Design of the 2015 ChaLearn AutoML challenge (2015) — 135 citations
• Results of the Active Learning Challenge (2011) — 102 citations
• Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters (2007) — 270 citations
• Agnostic Learning vs. Prior Knowledge Challenge (2007) — 38 citations
• Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki (2003) — 349 citations
• Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers (2003) — 440 citations