Muhammad Ferjad Naeem

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 59 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it; #21,478 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 19 citations into it
• Generative Models — 14 citations into it; #1,948 of 1,956 by trust here, #1,601 by citations

Whom Muhammad Ferjad Naeem trusts overall

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

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

Who trusts Muhammad Ferjad Naeem overall

1. Axel Sauer
2. Lucy Chai
3. Terrance Devries
4. Ahmed M. Alaa
5. Tuomas Kynkaanniemi
6. Stanislav Frolov
7. Arantxa Casanova
8. Utkarsh Ojha
9. Michal Drozdzal
10. Tim Pearce

Papers from Muhammad Ferjad Naeem's perspective

Ordered by how strongly the researchers Muhammad Ferjad Naeem 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Reliable Fidelity and Diversity Metrics for Generative Models (2020) — 603 citations
• Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness (2019) — 22 citations