Onur Avcı

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; #2,216 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; #4,796 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 Onur Avcı trusts overall

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

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

Who trusts Onur Avcı overall

1. Serkan Kiranyaz
2. Osama Abdeljaber
3. Mohsen Azimi
4. Arman Malekloo
5. Yuqing Gao
6. Turker Ince
7. Pei Cao
8. Armin Dadras Eslamlou
9. Moncef Gabbouj
10. Limin Sun

Papers from Onur Avcı's perspective

Ordered by how strongly the researchers Onur Avcı 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. 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. Dropout: a simple way to prevent neural networks from overfitting

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