Ioannis Manousos Katakis

Artificial Intelligence · 2007–2010

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 103 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #2,325 of 35,345 by trust here, #5,317 by citations
• Information Systems — 14 citations into it; #1,068 of 8,771 by trust here, #1,489 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Molecular Biology — 10 citations into it; #399 of 17,773 by trust here, #5,764 by citations
• Evaluation & Benchmarks — 7 citations into it; #341 of 3,147 by trust here, #1,233 by citations
• Natural Language Processing — 6 citations into it

Whom Ioannis Manousos Katakis trusts overall

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

1. Fabrizio Sebastiani
2. Ralf Klinkenberg
3. Thorsten Joachims
4. Alexey Tsymbal
5. Vladimir Naumovich Vapni
6. Geoff Hulten
7. Min-Ling Zhang
8. Zhi-Hua Zhou
9. J. Kolter
10. Charu C. Aggarwal

Who trusts Ioannis Manousos Katakis overall

1. Indrė Žliobaitė
2. Sun-Yuan Kung
3. Grigorios Tsoumakas
4. Jesse Read
5. Mohammad S. Sorower
6. Weiwei Cheng
7. Krzysztof Dembczynski
8. Min-Ling Zhang
9. Francisco Charte
10. Timothy N. Rubin

Papers from Ioannis Manousos Katakis's perspective

Ordered by how strongly the researchers Ioannis Manousos Katakis 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-Label Classification: An Overview (2007) — 2,985 citations
• Mining Multi-label Data (2010) — 1,592 citations
• Tracking recurring contexts using ensemble classifiers: an application to email filtering (2009) — 229 citations
• Incremental Clustering for the Classification of Concept-Drifting Data Streams (2008)