Haytham M. Fayek

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 274 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #18,589 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 53 citations into it
• Signal Processing — 27 citations into it; #3,811 of 7,572 by trust here, #4,560 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Meta & Continual Learning — 19 citations into it
• Model Architectures — 15 citations into it
• Experimental and Cognitive Psychology — 12 citations into it; #3,799 of 9,731 by trust here, #5,956 by citations
• Optimization & Training — 12 citations into it

Whom Haytham M. Fayek trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Alex Krizhevsky
7. Andrew Zisserman
8. Yann LeCun
9. Paul J. Werbos
10. X. Zhang

Who trusts Haytham M. Fayek overall

1. T. T. Gomes
2. Mustaqeem
3. Leonardo Pepino
4. Arsha Nagrani
5. Soonil Kwon
6. Pablo Ernesto Riera
7. Shan Yang

Papers from Haytham M. Fayek's perspective

Ordered by how strongly the researchers Haytham M. Fayek 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. 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data (2020) — 39 citations
• Progressive learning: A deep learning framework for continual learning (2020) — 85 citations
• Knowledge Capture and Replay for Continual Learning (2020) — 20 citations
• Evaluating deep learning architectures for Speech Emotion Recognition (2017) — 540 citations