Shervin Minaee

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 5 papers; trusts 995 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 380 citations into it; #32,684 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 287 citations into it; #11,368 of 21,575 by trust here, #9,722 by citations
• Natural Language Processing — 180 citations into it
• Large Language Models — 93 citations into it
• Model Architectures — 71 citations into it
• Evaluation & Benchmarks — 53 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Experimental and Cognitive Psychology — 22 citations into it; #3,691 of 9,731 by trust here, #9,016 by citations

Whom Shervin Minaee trusts overall

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

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

Who trusts Shervin Minaee overall

1. Fatemeh Haghighat
2. Smith K. Khare
3. Zijun Cui
4. Pengcheng He
5. Thorsten Hoeser
6. S. Suganyadevi
7. Jun Ma
8. Xiaodong Liu
9. Lucas Prado Osco
10. Joseph Paul Cohen

Papers from Shervin Minaee's perspective

Ordered by how strongly the researchers Shervin Minaee 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Image Segmentation Using Deep Learning: A Survey (2020) — 3,812 citations
• Deep Learning--based Text Classification (2020) — 1,342 citations
• Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning (2020) — 891 citations
• Large Language Models: A Survey (2024) — 1,030 citations
• Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network (2019) — 669 citations