Karim Ahmed

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 784 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #6,172 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 102 citations into it; #4,989 of 21,575 by trust here, #8,952 by citations
• Model Architectures — 60 citations into it; #615 of 2,278 by trust here, #1,300 by citations
• Optimization & Training — 31 citations into it; #1,073 of 2,601 by trust here, #2,081 by citations
• Natural Language Processing — 21 citations into it; #4,173 of 5,688 by trust here, #3,977 by citations
• Efficiency & Compression — 20 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Karim Ahmed trusts overall

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

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

Who trusts Karim Ahmed overall

1. Mark Sandler
2. Yunpeng Chen
3. Xin He
4. Xinyu Gong
5. Lin Wang
6. Peter Hase
7. Radwa El Shawi
8. Senthil Purushwalkam
9. David R. So
10. Andrew G. Howard

Papers from Karim Ahmed's perspective

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

Their papers

• MaskConnect: Connectivity Learning by Gradient Descent (2018) — 52 citations
• Network of Experts for Large-Scale Image Categorization (2016) — 142 citations
• Connectivity Learning in Multi-Branch Networks (2017) — 28 citations
• Weighted Transformer Network for Machine Translation (2017) — 142 citations
• BranchConnect: Image Categorization with Learned Branch Connections (2017) — 5 citations