Alireza Shafaei

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 509 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #20,062 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 70 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Evaluation & Benchmarks — 12 citations into it; #1,827 of 3,147 by trust here, #2,545 by citations
• Model Architectures — 10 citations into it
• Aerospace Engineering — 9 citations into it
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Alireza Shafaei trusts overall

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

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

Who trusts Alireza Shafaei overall

1. Yinhe Zheng
2. Alexander Meinke
3. Guanyi Chen
4. Jie Jessie Ren
5. Saikiran Bulusu
6. Haoqi Wang
7. Tuan-Hung Vu
8. Eric T. Nalisnick
9. Joseph Paul Cohen
10. Minlie Huang

Papers from Alireza Shafaei's perspective

Ordered by how strongly the researchers Alireza Shafaei 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Play and Learn: Using Video Games to Train Computer Vision Models (2016) — 116 citations
• Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors (2018) — 61 citations
• A Less Biased Evaluation of Out-of-distribution Sample Detectors (2018) — 88 citations