Joe Naoum-Sawaya

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 4 papers; trusts 67 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Statistics and Probability — 23 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Optimization & Training — 16 citations into it
• Management Science and Operations Research — 14 citations into it
• Statistical Learning Theory — 12 citations into it
• Algorithms & Complexity — 8 citations into it

Whom Joe Naoum-Sawaya trusts overall

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

1. Alysson M. Costa
2. Yann LeCun
3. Dimitris J. Bertsimas
4. Alex Krizhevsky
5. Xiaojin Zhu
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Kaiming He
9. Cynthia Barnhart
10. Kristin P. Bennett

Who trusts Joe Naoum-Sawaya overall

1. Berk Ustun
2. Cynthia Rudin
3. Ragheb Rahmaniani
4. Hai Wang
5. Teodor Gabriel Crainic
6. Michel Gendreau

Papers from Joe Naoum-Sawaya's perspective

Ordered by how strongly the researchers Joe Naoum-Sawaya 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Optimization Models for Machine Learning: A Survey (2019) — 261 citations
• High dimensional data classification and feature selection using support vector machines (2018) — 304 citations
• The shared-taxi problem: Formulation and solution methods (2014) — 189 citations
• An interior-point Benders based branch-and-cut algorithm for mixed integer programs (2010) — 80 citations