Ş. Ertekin

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 5 papers; trusts 85 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #10,390 of 35,345 by trust here, #17,110 by citations
• Statistical Learning Theory — 33 citations into it
• Information Systems — 29 citations into it; #3,681 of 8,771 by trust here, #6,770 by citations
• Optimization & Training — 21 citations into it
• Computational Theory and Mathematics — 18 citations into it; #11,176 of 12,182 by trust here, #9,804 by citations
• Algorithms & Complexity — 16 citations into it; #490 of 516 by trust here, #452 by citations
• Safety, Robustness & Interpretability — 15 citations into it
• Statistics and Probability — 12 citations into it

Whom Ş. Ertekin trusts overall

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

1. Cynthia Rudin
2. Vladimir Naumovich Vapnik
3. Berk Ustun
4. Jerome H. Friedman
5. R. Rivest
6. Yoav Freund
7. J. Ross Quinlan
8. Robert E. Schapire
9. Alex Alves Freitas
10. Nicolai Meinshausen

Who trusts Ş. Ertekin overall

1. Cynthia Rudin
2. Haibo He
3. Berk Ustun
4. Francis R. Bach
5. Edwardo A. Garcia
6. Aditya Krishna Menon
7. Yoshua Bengio
8. Rung Ching Chen
9. L. Eon Bottou
10. Nataliya Sokolovska

Papers from Ş. Ertekin's perspective

Ordered by how strongly the researchers Ş. Ertekin 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. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• On Equivalence Relationships Between Classification and Ranking Algorithms (2011) — 29 citations
• Learning customized and optimized lists of rules with mathematical programming (2018) — 56 citations
• A Bayesian Approach to Learning Scoring Systems (2015) — 13 citations
• Learning on the border: active learning in imbalanced data classification (2007) — 449 citations
• Fast Kernel Classifiers with Online and Active Learning (2005) — 724 citations