Santiago A. Cadena

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 40 citations into it
• Cognitive Neuroscience — 23 citations into it
• Artificial Intelligence — 20 citations into it
• Generative Models — 11 citations into it
• Model Architectures — 11 citations into it
• Neuroscience — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Biophysics — 8 citations into it

Whom Santiago A. Cadena trusts overall

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

1. Diederik P. Kingma
2. Nikolaus Kriegeskorte
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Aäron van den Oord
6. Yoshua Bengio
7. Alex Krizhevsky
8. Leon A. Gatys
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Santiago A. Cadena overall

1. Grace W. Lindsay
2. Fabian H Sinz
3. Saurabh Saxena
4. Pouya Bashivan

Papers from Santiago A. Cadena's perspective

Ordered by how strongly the researchers Santiago A. Cadena 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Diverse feature visualizations reveal invariances in early layers of deep neural networks (2018) — 34 citations
• Deep convolutional models improve predictions of macaque V1 responses to natural images (2017) — 402 citations