Pavel Berkhin

Artificial Intelligence · 2005–2006

A researcher in the Science corpus: 2 papers; trusts 77 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #2,608 of 35,345 by trust here, #10,798 by citations
• Information Systems — 22 citations into it; #1,655 of 8,771 by trust here, #5,942 by citations
• Signal Processing — 18 citations into it; #275 of 7,572 by trust here, #1,411 by citations
• Statistical and Nonlinear Physics — 13 citations into it; #2,016 of 10,127 by trust here, #6,421 by citations
• Computer Networks and Communications — 9 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Information Theory — 6 citations into it
• Information Retrieval & Search — 5 citations into it

Whom Pavel Berkhin trusts overall

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

1. Jerome H. Friedman
2. Inderjit S. Dhillon
3. George Karypis
4. Ronny Lempel
5. Thomas Hofmann
6. Tian Zhang
7. Teuvo Kohonen
8. Taher H. Haveliwala
9. David E. Goldberg
10. Daniel Boley

Who trusts Pavel Berkhin overall

1. Erik Cambria
2. David F. Gleich
3. Joseph M Hellerstein
4. Benjamin M. Marlin
5. Yuri Malitsky
6. Hila Becker
7. Kasun Bandara
8. Brendan Tran Morris
9. Hengshuai Yao
10. Gunhee Kim

Papers from Pavel Berkhin's perspective

Ordered by how strongly the researchers Pavel Berkhin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• A Survey of Clustering Data Mining Techniques (2006) — 3,367 citations
• A Survey on PageRank Computing (2005) — 496 citations