Aidan N. Gomez

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 8 papers; trusts 300 researchers and is trusted by 1,675 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #4,228 of 35,345 by trust here, #3,072 by citations
• Computer Vision and Pattern Recognition — 96 citations into it; #2,823 of 21,575 by trust here, #2,373 by citations
• Model Architectures — 61 citations into it; #38 of 2,278 by trust here, #16 by citations
• Natural Language Processing — 56 citations into it; #58 of 5,688 by trust here, #17 by citations
• Molecular Biology — 53 citations into it
• Computational Biology & Bioinformatics — 23 citations into it
• Optimization & Training — 23 citations into it
• Generative Models — 20 citations into it

Whom Aidan N. Gomez trusts overall

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

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

Who trusts Aidan N. Gomez overall

1. Felix Stahlberg
2. Yoshua Bengio
3. Mohit Bansal
4. Xu Tan
5. Yi Tay
6. Ronghang Hu
7. Zhe Gan
8. Kyunghyun Cho
9. Quoc V. Le
10. Percy Liang

Papers from Aidan N. Gomez's perspective

Ordered by how strongly the researchers Aidan N. Gomez 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

• The Reversible Residual Network: Backpropagation Without Storing Activations (2017) — 654 citations
• One Model To Learn Them All (2017) — 350 citations
• Tensor2Tensor for Neural Machine Translation (2018) — 553 citations
• Depthwise Separable Convolutions for Neural Machine Translation (2017) — 314 citations
• Disease variant prediction with deep generative models of evolutionary data (2021) — 734 citations
• Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning (2020) — 21 citations
• A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks (2019) — 125 citations
• Attention is All you Need (2017) — 185,508 citations