Nicola L. C. Talbot

Artificial Intelligence · 2003–2010

A researcher in the Science corpus: 3 papers; trusts 44 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #5,139 of 35,345 by trust here, #9,557 by citations
• Statistical Learning Theory — 14 citations into it; #270 of 1,038 by trust here, #349 by citations
• Statistics and Probability — 13 citations into it
• Optimization & Training — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #16,036 of 21,575 by trust here, #17,258 by citations
• Probabilistic & Bayesian Methods — 8 citations into it
• Evaluation & Benchmarks — 5 citations into it; #562 of 3,147 by trust here, #1,467 by citations
• Model Architectures — 5 citations into it

Whom Nicola L. C. Talbot trusts overall

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

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

Who trusts Nicola L. C. Talbot overall

1. Gavin C. Cawley
2. Jakramate Bootkrajang
3. Isabelle M Guyon
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 Nicola L. C. Talbot's perspective

Ordered by how strongly the researchers Nicola L. C. Talbot 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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
• Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters (2007) — 270 citations
• Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers (2003) — 440 citations