Haikel Salem Alhichri

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 144 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it
• Artificial Intelligence — 39 citations into it; #31,851 of 35,345 by trust here, #33,294 by citations
• Media Technology — 39 citations into it; #3,144 of 3,621 by trust here, #2,178 by citations
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Haikel Salem Alhichri trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Kaiming He
6. J. Urgen Schmidhuber
7. Yi Yang
8. Yann LeCun
9. Diederik P. Kingma
10. Ross B. Girshick

Who trusts Haikel Salem Alhichri overall

1. Nassim Ammour

Papers from Haikel Salem Alhichri's perspective

Ordered by how strongly the researchers Haikel Salem Alhichri 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021) — 298 citations
• Continual Learning Approach for Remote Sensing Scene Classification (2020) — 20 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