Omer Sagi

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 52 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #9,569 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 12 citations into it
• Information Systems — 11 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Signal Processing — 9 citations into it; #1,678 of 7,572 by trust here, #3,566 by citations
• Statistics and Probability — 7 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Omer Sagi trusts overall

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

1. Leo Breiman
2. Thomas G. Dietterich
3. Robert E. Schapire
4. Pedro M. Domingos
5. Yoav Freund
6. Tin Kam Ho
7. Jerome H. Friedman
8. Nikunj C. Oza
9. Lior Rokach
10. Geoffrey E. Hinton

Who trusts Omer Sagi overall

1. John T. Hancock
2. Ammar Mohammed
3. Murray Shanahan
4. Dongfu Jiang
5. Hristos Tyralis
6. M. A. Ganaie
7. Vincent S. Chen
8. Taghi M. Khoshgoftaar
9. Rania Kora
10. Jakob Gawlikowski

Papers from Omer Sagi's perspective

Ordered by how strongly the researchers Omer Sagi 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Ensemble learning: A survey (2018) — 3,015 citations
• Approximating XGBoost with an interpretable decision tree (2021) — 499 citations