Alex Hernandez-Garcia

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 344 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #13,374 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #8,749 of 21,575 by trust here, #14,151 by citations
• Optimization & Training — 23 citations into it
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 17 citations into it; #1,891 of 3,145 by trust here, #2,691 by citations
• Safety, Robustness & Interpretability — 6 citations into it

Whom Alex Hernandez-Garcia trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Yann LeCun
7. Xavier Glorot
8. X. Zhang
9. Ian J. Goodfellow
10. Diederik P. Kingma

Who trusts Alex Hernandez-Garcia overall

1. Raphael Gontijo Lopes
2. Hamed Ghoddusi
3. Sylvia J. Smullin
4. Sungbin Lim
5. Ekin Dogus Cubuk
6. Peter König
7. Shuxiao Chen
8. Germán G. Creamer
9. Ethan Dyer
10. Ildoo Kim

Papers from Alex Hernandez-Garcia's perspective

Ordered by how strongly the researchers Alex Hernandez-Garcia 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. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Data augmentation instead of explicit regularization (2018) — 166 citations
• Further Advantages of Data Augmentation on Convolutional Neural Networks (2018) — 126 citations