Michael W. Berry

Artificial Intelligence · 2003–2007

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #9,187 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 14 citations into it; #5,022 of 21,575 by trust here, #5,565 by citations
• Natural Language Processing — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Signal Processing — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it; #2,486 of 12,182 by trust here, #1,269 by citations
• Optimization & Training — 8 citations into it
• Computational Mechanics — 6 citations into it; #3,849 of 10,001 by trust here, #2,112 by citations

Whom Michael W. Berry trusts overall

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

1. Patrik O. Hoyer
2. Daniel D. Lee
3. Chih-Jen Lin
4. P. Paatero
5. Farial Shahnaz
6. Andrzej S. Cichocki
7. V. P. Pauca
8. Inderjit S. Dhillon
9. Aapo Hyvärinen
10. Beresford N. Parlett

Who trusts Michael W. Berry overall

1. Hyunsoo Kim
2. Haesun Park
3. Jingu Kim
4. Andrzej S. Cichocki
5. Nicolas Gillis
6. Yangyang Xu
7. Naiyang Guan
8. Chris H. Q. Ding
9. Leilani H. Gilpin
10. Sen Jia

Papers from Michael W. Berry's perspective

Ordered by how strongly the researchers Michael W. Berry 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. Learning the parts of objects by non-negative matrix factorization
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. ImageNet: A large-scale hierarchical image database

Their papers

• Algorithms and applications for approximate nonnegative matrix factorization (2007) — 1,648 citations
• Document clustering using nonnegative matrix factorization (2006) — 681 citations
• Text Mining Using Non-Negative Matrix Factorizations (2004) — 365 citations
• Survey of Text Mining: Clustering, Classification, and Retrieval (2007) — 535 citations
• Survey of Text Mining (2003) — 378 citations