Gamaleldin F. Elsayed

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 7 papers; trusts 221 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 195 citations into it; #29,612 of 35,345 by trust here, #5,970 by citations
• Computer Vision and Pattern Recognition — 184 citations into it
• Safety, Robustness & Interpretability — 72 citations into it; #1,796 of 2,251 by trust here, #2,110 by citations
• Model Architectures — 39 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Cognitive Neuroscience — 27 citations into it
• Optimization & Training — 18 citations into it
• Signal Processing — 18 citations into it

Whom Gamaleldin F. Elsayed trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ian J. Goodfellow
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ronald J. Williams
7. Kaiming He
8. Christian Szegedy
9. Yann LeCun
10. Ilya Sutskever

Who trusts Gamaleldin F. Elsayed overall

1. Alexia Jolicoeur-Martineau
2. Karen Hambardzumyan
3. Colin Wei
4. Hyojin Bahng
5. W. Zhang
6. Paarth Neekhara
7. Ioannis Mitliagkas
8. Gavin Weiguang Ding
9. João Dias Semedo
10. Tengyu Ma

Papers from Gamaleldin F. Elsayed's perspective

Ordered by how strongly the researchers Gamaleldin F. Elsayed 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• Large Margin Deep Networks for Classification (2018) — 328 citations
• Reorganization between preparatory and movement population responses in motor cortex (2016) — 409 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Saccader: Improving Accuracy of Hard Attention Models for Vision (2019) — 82 citations
• Conditional Object-Centric Learning from Video (2021) — 314 citations
• Adversarial Reprogramming of Neural Networks (2018) — 204 citations