S. Kotsiantis

Artificial Intelligence · 2004–2020

A researcher in the Science corpus: 9 papers; trusts 237 researchers and is trusted by 122 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 205 citations into it; #9,077 of 35,345 by trust here, #10,207 by citations
• Safety, Robustness & Interpretability — 85 citations into it
• Information Systems — 48 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Computational Theory and Mathematics — 32 citations into it
• Evaluation & Benchmarks — 25 citations into it; #226 of 3,147 by trust here, #1,233 by citations
• Statistical Learning Theory — 25 citations into it
• Statistics and Probability — 23 citations into it

Whom S. Kotsiantis trusts overall

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

1. Thomas G. Dietterich
2. João Gama
3. Leo Breiman
4. Yoav Freund
5. William W. Cohen
6. J. Ross Quinlan
7. Sreerama K. Murthy
8. Koby Crammer
9. Geoffrey I. Webb
10. David Maxwell Chickering

Who trusts S. Kotsiantis overall

1. Syeda Manjia Tahsien
2. John T. Hancock
3. Bertie Vidgen
4. Lida Abdi
5. Matthew J. Cracknell
6. Baljinder Ghotra
7. Kwetishe Joro Danjuma
8. Blerina Lika
9. Eija Honkavaara
10. Javed Asharf

Papers from S. Kotsiantis's perspective

Ordered by how strongly the researchers S. Kotsiantis 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Explainable AI: A Review of Machine Learning Interpretability Methods (2020) — 2,690 citations
• Decision trees: a recent overview (2011) — 1,429 citations
• Data Preprocessing for Supervised Leaning (2007) — 1,041 citations
• Supervised Machine Learning: A Review of Classification Techniques (2007) — 5,040 citations
• Machine learning: a review of classification and combining techniques (2006) — 1,442 citations
• Association Rules Mining: A Recent Overview (2006) — 667 citations
• Handling imbalanced datasets: A review (2006) — 1,061 citations
• Discretization Techniques: A recent survey (2006) — 477 citations
• PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES (2004) — 333 citations