Ali Cheraghian

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 637 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it; #11,804 of 21,575 by trust here, #8,722 by citations
• Artificial Intelligence — 109 citations into it; #18,782 of 35,345 by trust here, #12,580 by citations
• Representation & Self-Supervised Learning — 54 citations into it
• Meta & Continual Learning — 34 citations into it
• Radiology, Nuclear Medicine and Imaging — 31 citations into it
• Computational Mechanics — 29 citations into it
• Geology — 23 citations into it
• Aerospace Engineering — 16 citations into it; #3,803 of 6,421 by trust here, #5,612 by citations

Whom Ali Cheraghian trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ilya Sutskever
6. Tomas Mikolov
7. Sergey Ioffe
8. Yoshua Bengio
9. L. V. D. van der Maaten
10. Christian Szegedy

Who trusts Ali Cheraghian overall

1. Shafin Rahman
2. Da-Wei Zhou
3. Jaya Kalla
4. Song Tian
5. Christian Simon
6. Lars Petersson
7. Zhixiang Chi
8. Dylan Campbell
9. Yabin Wang
10. Mert Kilickaya

Papers from Ali Cheraghian's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (2021) — 233 citations
• Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021) — 81 citations
• Transductive Zero-Shot Learning for 3D Point Cloud Classification (2019) — 68 citations
• Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects (2019) — 48 citations
• Zero-shot Learning of 3D Point Cloud Objects (2019) — 66 citations