Ioannis P. Vlahavas

Artificial Intelligence · 2003–2017

A researcher in the Science corpus: 9 papers; trusts 64 researchers and is trusted by 66 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #3,221 of 35,345 by trust here, #5,970 by citations
• Information Systems — 29 citations into it; #835 of 8,771 by trust here, #968 by citations
• Computer Vision and Pattern Recognition — 24 citations into it; #4,246 of 21,575 by trust here, #9,872 by citations
• Molecular Biology — 19 citations into it; #851 of 17,773 by trust here, #9,030 by citations
• Evaluation & Benchmarks — 14 citations into it; #547 of 3,147 by trust here, #2,040 by citations
• Optimization & Training — 9 citations into it
• Statistics and Probability — 9 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Ioannis P. Vlahavas trusts overall

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

1. Grigorios Tsoumakas
2. Min-Ling Zhang
3. Zhi-Hua Zhou
4. David Hilton Wolpert
5. Fabrizio Sebastiani
6. R. Caruana
7. Thorsten Joachims
8. Robert E. Schapire
9. Jaka Demsar
10. Leo Breiman

Who trusts Ioannis P. Vlahavas overall

1. Francisco Charte
2. Min-Ling Zhang
3. Jesse Read
4. Mohammad S. Sorower
5. Grigorios Tsoumakas
6. Antonio Jesús Rivera
7. Krzysztof Dembczynski
8. Arto Klami
9. M. J. Jesús
10. A. D. Sá

Papers from Ioannis P. Vlahavas's perspective

Ordered by how strongly the researchers Ioannis P. Vlahavas 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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

• Random k -Labelsets: An Ensemble Method for Multilabel Classification (2007) — 1,001 citations
• Multi-target regression via input space expansion: treating targets as inputs (2012) — 367 citations
• Mining Multi-label Data (2010) — 1,592 citations
• On the Stratification of Multi-label Data (2011) — 553 citations
• Machine Learning and Data Mining Methods in Diabetes Research (2017) — 1,239 citations
• Tracking recurring contexts using ensemble classifiers: an application to email filtering (2009) — 229 citations
• Multi-Label Classification of Music into Emotions (2008) — 811 citations
• Incremental Clustering for the Classification of Concept-Drifting Data Streams (2008)
• "Cultures in negotiation": teachers' acceptance/resistance attitudes considering the infusion of technology into schools (2003) — 296 citations