Tony Van Gestel

Artificial Intelligence · 2001–2008

A researcher in the Science corpus: 9 papers; trusts 17 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #6,760 of 35,345 by trust here, #5,924 by citations
• Statistical Learning Theory — 15 citations into it
• Control and Systems Engineering — 14 citations into it; #5,188 of 8,257 by trust here, #759 by citations
• Model Architectures — 12 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Scaling & Learning Theory — 8 citations into it

Whom Tony Van Gestel trusts overall

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

1. Johan A. K. Suykens
2. David John Cameron MacKay
3. Vladimir Naumovich Vapni
4. Christopher K. I. Williams
5. Yuhai Wu
6. Dustin Boswell
7. Bernhard Schölkopf
8. Joos Vandewalle
9. Federico Girosi
10. Craig John Saunders

Who trusts Tony Van Gestel overall

1. David Martens
2. Bart Baesens
3. Emilio Carrizosa
4. Yu Zhang
5. Lionel Gueguen
6. Arash Bahrammirzaee
7. Johan A. K. Suykens
8. Jieping Ye
9. Yongqiao Wang
10. Xiaodong Li

Papers from Tony Van Gestel's perspective

Ordered by how strongly the researchers Tony Van Gestel 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Automatic relevance determination for least squares support vector machine regression (2001) — 40 citations
• Financial time series prediction using least squares support vector machines within the evidence framework (2001) — 536 citations
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis (2002) — 311 citations
• Credit Risk Management: Basic Concepts: Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital (2008) — 126 citations
• Comprehensible credit scoring models using rule extraction from support vector machines (2007) — 475 citations
• Benchmarking Least Squares Support Vector Machine Classifiers (2004) — 535 citations
• Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines (2003) — 319 citations
• Benchmarking state-of-the-art classification algorithms for credit scoring (2003) — 416 citations
• Least Squares Support Vector Machines (2002) — 1,828 citations