Willem J. Melssen

Artificial Intelligence · 2003–2007

A researcher in the Science corpus: 4 papers; trusts 7 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 8 citations into it; #28,678 of 35,345 by trust here, #31,361 by citations
• Statistical Learning Theory — 8 citations into it
• Control and Systems Engineering — 7 citations into it; #6,677 of 8,257 by trust here, #3,865 by citations
• Analytical Chemistry — 5 citations into it; #505 of 1,254 by trust here, #338 by citations
• Biophysics — 5 citations into it
• Scaling & Learning Theory — 5 citations into it

Whom Willem J. Melssen trusts overall

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

1. Bernhard Schölkopf
2. Alex Smola
3. Roman Rosipal
4. Vladimir Naumovich Vapni
5. Robert N. Feudale
6. Yuhai Wu
7. Sebastian Mika

Who trusts Willem J. Melssen overall

1. Zerina Mašetić
2. Christoffer Axelsson
3. Igor Santos

Papers from Willem J. Melssen's perspective

Ordered by how strongly the researchers Willem J. Melssen 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. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Comparing support vector machines to PLS for spectral regression applications (2004) — 370 citations
• Visualisation and interpretation of Support Vector Regression models. (2007) — 164 citations
• Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel (2006) — 324 citations
• Using support vector machines for time series prediction (2003) — 398 citations