Lutgarde M. C. Buydens

Analytical Chemistry · 2000–2017

A researcher in the Science corpus: 9 papers; trusts 12 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Analytical Chemistry — 14 citations into it; #329 of 1,254 by trust here, #158 by citations
• Artificial Intelligence — 14 citations into it; #15,853 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 10 citations into it; #11,816 of 21,575 by trust here, #18,782 by citations
• Statistical Learning Theory — 10 citations into it
• Optimization & Training — 10 citations into it
• Control and Systems Engineering — 9 citations into it; #6,678 of 8,257 by trust here, #3,865 by citations
• Biophysics — 8 citations into it
• Scaling & Learning Theory — 5 citations into it

Whom Lutgarde M. C. Buydens trusts overall

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

1. Bernhard Schölkopf
2. Yann LeCun
3. Alex Smola
4. Yoshua Bengio
5. L. Eon Bottou
6. Vladimir Naumovich Vapni
7. Alex Krizhevsky
8. Geoffrey E. Hinton
9. Roman Rosipal
10. Yuhai Wu

Who trusts Lutgarde M. C. Buydens overall

1. Christoph Bergmeir
2. Ron Wehrens
3. Nima Aghaeepour
4. C. M. Andersen
5. Zerina Mašetić
6. Christoffer Axelsson
7. Johannes W. Kruisselbrink
8. José Manuel Benítez

Papers from Lutgarde M. C. Buydens's perspective

Ordered by how strongly the researchers Lutgarde M. C. Buydens 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Self- and Super-organizing Maps in R: The kohonen Package (2007) — 759 citations
• Comparing support vector machines to PLS for spectral regression applications (2004) — 370 citations
• The bootstrap: a tutorial (2000) — 585 citations
• Visualisation and interpretation of Support Vector Regression models. (2007) — 164 citations
• Convolutional neural networks for vibrational spectroscopic data analysis. (2017) — 406 citations
• Interpretation of variable importance in Partial Least Squares with Significance Multivariate Correlation (sMC) (2014) — 209 citations
• Detecting the addition of sugar and water to wine (2013) — 51 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