Osama Abdeljaber

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 14 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it
• Civil and Structural Engineering — 30 citations into it; #2,217 of 6,160 by trust here, #1,863 by citations
• Control and Systems Engineering — 26 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Mechanics of Materials — 25 citations into it; #4,797 of 6,291 by trust here, #2,227 by citations
• Mechanical Engineering — 20 citations into it
• Cardiology and Cardiovascular Medicine — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Osama Abdeljaber trusts overall

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

1. Geoffrey E. Hinton
2. Serkan Kiranyaz
3. Turker Ince
4. Alex Krizhevsky
5. Levent Tolga Eren
6. Yann LeCun
7. Ilya Sutskever
8. Yoshua Bengio
9. Thomas G. Dietterich
10. Onur Avcı

Who trusts Osama Abdeljaber overall

1. Serkan Kiranyaz
2. Onur Avcı
3. Mohsen Azimi
4. Yuqing Gao
5. Pei Cao
6. Armin Dadras Eslamlou
7. Limin Sun
8. Asifullah Khan
9. Turker Ince
10. David Verstraete

Papers from Osama Abdeljaber's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Dropout: a simple way to prevent neural networks from overfitting
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• 1D Convolutional Neural Networks and Applications: A Survey (2019) — 2,635 citations
• A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020) — 1,195 citations
• 1-D Convolutional Neural Networks for Signal Processing Applications (2019) — 297 citations
• 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data (2018) — 475 citations
• Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks (2017) — 1,158 citations