Fahad Sarfraz

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 238 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #16,625 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Optimization & Training — 26 citations into it
• Meta & Continual Learning — 17 citations into it; #603 of 1,041 by trust here, #897 by citations
• Efficiency & Compression — 13 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Model Architectures — 9 citations into it

Whom Fahad Sarfraz trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Yoshua Bengio
6. Sergey Zagoruyko
7. Christian Szegedy
8. Ilya Sutskever
9. Alex Graves
10. Diederik P. Kingma

Who trusts Fahad Sarfraz overall

1. Elahe Arani
2. Bahram Zonooz
3. Prashant Bhat
4. Quang Pham
5. Liyuan Wang
6. Zhen Wang
7. Lorenzo Bonicelli
8. Yichen Wu

Papers from Fahad Sarfraz's perspective

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

Their papers

• Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System (2022) — 177 citations
• Knowledge Distillation Beyond Model Compression (2020) — 46 citations
• Noise as a Resource for Learning in Knowledge Distillation (2020) — 18 citations