Naif Alajlan

Computer Vision and Pattern Recognition · 2007–2021

A researcher in the Science corpus: 9 papers; trusts 244 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #11,044 of 21,575 by trust here, #12,419 by citations
• Artificial Intelligence — 56 citations into it; #31,849 of 35,345 by trust here, #33,294 by citations
• Media Technology — 42 citations into it; #1,442 of 3,621 by trust here, #1,365 by citations
• Computational Mechanics — 28 citations into it; #5,123 of 10,001 by trust here, #5,567 by citations
• Mechanics of Materials — 26 citations into it
• Statistical and Nonlinear Physics — 25 citations into it
• Atmospheric Science — 15 citations into it
• Model Architectures — 15 citations into it

Whom Naif Alajlan trusts overall

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

1. Geoffrey E. Hinton
2. Thomas B. Sebastian
3. Alex Krizhevsky
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Serge J. Belongie
7. Yi Yang
8. Yann LeCun
9. Jorge Nocedal
10. Ilya Sutskever

Who trusts Naif Alajlan overall

1. Ming Yang
2. Souleyman Chaib
3. Gong Cheng
4. Kpalma Kidiyo
5. Nassim Ammour

Papers from Naif Alajlan's perspective

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

Their papers

• Shape retrieval using triangle-area representation and dynamic space warping (2007) — 300 citations
• Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems (2019) — 644 citations
• Land-Use Classification With Compressive Sensing Multifeature Fusion (2015) — 75 citations
• Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021) — 298 citations
• Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates (2020) — 395 citations
• Continual Learning Approach for Remote Sensing Scene Classification (2020) — 20 citations
• Sensitivity and uncertainty analysis for flexoelectric nanostructures (2018) — 218 citations
• Deep learning approach for active classification of electrocardiogram signals (2016) — 601 citations
• Using convolutional features and a sparse autoencoder for land-use scene classification (2016) — 153 citations