Vladimir Cherkassky

Artificial Intelligence · 1997–2009

A researcher in the Science corpus: 5 papers; trusts 14 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 11 citations into it; #22,797 of 35,345 by trust here, #10,566 by citations
• Statistics and Probability — 10 citations into it; #1,443 of 12,932 by trust here, #2,108 by citations
• Statistical Learning Theory — 9 citations into it
• Control and Systems Engineering — 8 citations into it; #5,359 of 8,257 by trust here, #7,501 by citations
• Computer Vision and Pattern Recognition — 6 citations into it; #4,091 of 21,575 by trust here, #4,584 by citations
• Scaling & Learning Theory — 6 citations into it

Whom Vladimir Cherkassky trusts overall

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

1. Vladimir Naumovich Vapni
2. Bernhard Schölkopf
3. Robert A. Lordo
4. Alex Smola
5. Yuhai Wu
6. Trevor J. Hastie
7. C. Burges
8. Jerome H. Friedman
9. Robert Tibshirani
10. Johan A. K. Suykens

Who trusts Vladimir Cherkassky overall

1. Paulo Cortez
2. Ergun Biçici
3. Lei Zhang
4. Martin B. Haugh
5. Steven Van Vaerenbergh
6. Felipe Antonio Chegury Viana
7. Ajay Kalra
8. Matteo Pastorino
9. Asaf Manela
10. Franccois Fleuret

Papers from Vladimir Cherkassky's perspective

Ordered by how strongly the researchers Vladimir Cherkassky 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Practical selection of SVM parameters and noise estimation for SVM regression (2004) — 2,016 citations
• Another look at statistical learning theory and regularization (2009) — 53 citations
• Support vector machines for temporal classification of block design fMRI data (2005) — 428 citations
• A combined SVM and LDA approach for classification (2005) — 896 citations
• The Nature Of Statistical Learning Theory (1997)