Ali Hassani

Computer Vision and Pattern Recognition · 2021–2022

A researcher in the Science corpus: 4 papers; trusts 868 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 173 citations into it; #18,438 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 134 citations into it; #30,461 of 35,345 by trust here, #17,412 by citations
• Model Architectures — 76 citations into it; #1,896 of 2,278 by trust here, #1,378 by citations
• Optimization & Training — 20 citations into it
• Natural Language Processing — 17 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Large Language Models — 15 citations into it
• Efficiency & Compression — 12 citations into it

Whom Ali Hassani trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Ilya Sutskever
6. Yoshua Bengio
7. Yann LeCun
8. Diederik P. Kingma
9. Karen Simonyan
10. Andrew Zisserman

Who trusts Ali Hassani overall

1. Jiayu Jiao
2. Zhen Wang
3. Yanyu Li
4. Jitesh Jain
5. C. Yuhua
6. Zizhao Zhang
7. Yuyao Tang
8. Jiacheng Li
9. Zhihao Duan
10. Liu Liu

Papers from Ali Hassani's perspective

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

Their papers

• OneFormer: One Transformer to Rule Universal Image Segmentation (2022) — 605 citations
• Escaping the Big Data Paradigm with Compact Transformers (2021) — 585 citations
• Neighborhood Attention Transformer (2022) — 500 citations
• Dilated Neighborhood Attention Transformer (2022) — 120 citations