Faruk Ahmed

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 310 researchers and is trusted by 2,285 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 85 citations into it; #837 of 21,575 by trust here, #735 by citations
• Artificial Intelligence — 76 citations into it; #953 of 35,345 by trust here, #1,012 by citations
• Generative Models — 45 citations into it; #108 of 1,956 by trust here, #72 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Optimization & Training — 10 citations into it; #225 of 2,601 by trust here, #108 by citations
• Model Architectures — 8 citations into it

Whom Faruk Ahmed trusts overall

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

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

Who trusts Faruk Ahmed overall

1. Yoshua Bengio
2. Jiaming Song
3. Stefano Ermon
4. Fei Ye
5. Shengjia Zhao
6. Shiyu Chang
7. Sergey Levine
8. Dacheng Tao
9. Alexia Jolicoeur-Martineau
10. Trevor Darrell

Papers from Faruk Ahmed's perspective

Ordered by how strongly the researchers Faruk 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Improved Training of Wasserstein GANs (2017) — 11,031 citations
• PixelVAE: A Latent Variable Model for Natural Images (2016) — 362 citations
• Reducing Overfitting in Deep Networks by Decorrelating Representations (2015) — 461 citations
• Detecting semantic anomalies (2019) — 98 citations