Guillermo Valle Pérez

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 44 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #26,275 of 35,345 by trust here, #21,878 by citations
• Scaling & Learning Theory — 28 citations into it; #322 of 428 by trust here, #336 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #16,110 of 21,575 by trust here, #14,478 by citations
• Optimization & Training — 14 citations into it
• Statistical Learning Theory — 7 citations into it
• Model Architectures — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Guillermo Valle Pérez trusts overall

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

1. J. Urgen Schmidhuber
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. L. Eon Bottou
6. Yann LeCun
7. Jimmy Ba
8. Ilya Sutskever
9. Vladimir Naumovich Vapni
10. Diederik P. Kingma

Who trusts Guillermo Valle Pérez overall

1. Greg Yang
2. Hadi Salman
3. Katherine L. Hermann
4. Maximilian Pichler

Papers from Guillermo Valle Pérez's perspective

Ordered by how strongly the researchers Guillermo Valle Pérez 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning generalizes because the parameter-function map is biased towards simple functions (2018) — 293 citations