Sebastian Mika

Artificial Intelligence · 1998–2009

A researcher in the Science corpus: 10 papers; trusts 13 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #5,365 of 35,345 by trust here, #3,582 by citations
• Statistical Learning Theory — 26 citations into it; #409 of 1,038 by trust here, #131 by citations
• Optimization & Training — 12 citations into it
• Statistics and Probability — 11 citations into it
• Scaling & Learning Theory — 10 citations into it
• Control and Systems Engineering — 7 citations into it

Whom Sebastian Mika trusts overall

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

1. Bernhard Schölkopf
2. Alex Smola
3. C. Burges
4. Vladimir Naumovich Vapni
5. Bernhard E. Boser
6. Yuhai Wu
7. Christopher K. I. Williams
8. Federico Girosi
9. Yann LeCun
10. Yoav Freund

Who trusts Sebastian Mika overall

1. K. Müller
2. Grgoire Montavon
3. Paul Honeine
4. Gavin C. Cawley
5. Andrea Vedaldi
6. Ronan Collobert
7. Christoph H. Lampert
8. Bernhard Schölkopf
9. Dacheng Tao
10. Qiang Yang

Papers from Sebastian Mika's perspective

Ordered by how strongly the researchers Sebastian Mika 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

• A kernel view of the dimensionality reduction of manifolds (2004) — 632 citations
• An introduction to kernel-based learning algorithms (2001) — 3,777 citations
• On the Convergence of Leveraging (2001) — 47 citations
• Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites (2000) — 477 citations
• Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules (2007) — 59 citations
• Benchmark Data Set for in Silico Prediction of Ames Mutagenicity (2009) — 352 citations
• Regularized Principal Manifolds (1999)
• Fisher discriminant analysis with kernels (1999)
• Input space versus feature space in kernel-based methods (1999)
• Kernel PCA and De-Noising in Feature Spaces (1998)