Olivier Caelen

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 30 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #12,383 of 35,345 by trust here, #10,942 by citations
• Information Systems — 41 citations into it
• Accounting — 22 citations into it; #8,924 of 8,970 by trust here, #5,666 by citations
• Optimization & Training — 15 citations into it
• Statistics and Probability — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Statistical Learning Theory — 10 citations into it
• Model Architectures — 10 citations into it

Whom Olivier Caelen trusts overall

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

1. Nitesh V. Chawla
2. Nathalie Japkowicz
3. Charles Peter Elkan
4. David J. Hand
5. Andrea Dal Pozzolo
6. Radford M. Neal
7. William W. Cohen
8. Jon T. S. Quah
9. Bianca Zadrozny
10. Gerhard Widmer

Who trusts Olivier Caelen overall

1. Andrea Dal Pozzolo
2. Hyun-Jae Choi
3. Gianluca Bontempi
4. Sergey I. Nikolenko
5. A. Taha
6. Yaya Heryadi
7. Eric Jang
8. James Jordon
9. Noveen Sachdeva
10. Ksenia Konyushkova

Papers from Olivier Caelen's perspective

Ordered by how strongly the researchers Olivier Caelen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Et al
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Calibrating Probability with Undersampling for Unbalanced Classification (2015) — 681 citations
• Sequence classification for credit-card fraud detection (2018) — 465 citations
• Combining unsupervised and supervised learning in credit card fraud detection (2019) — 452 citations
• Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018) — 554 citations
• APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions (2015) — 371 citations
• Learned lessons in credit card fraud detection from a practitioner perspective (2014) — 537 citations