Nicolás Guil Mata

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 68 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #3,383 of 35,345 by trust here, #3,009 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #3,209 of 21,575 by trust here, #2,263 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 9 citations into it
• Biomedical Engineering — 7 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Nicolás Guil Mata trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Max Welling
4. Yoshua Bengio
5. Kaiming He
6. Karen Simonyan
7. Yann LeCun
8. Diederik P. Kingma
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Nicolás Guil Mata overall

1. Ali Ayub
2. Jiangpeng He
3. Eden Belouadah
4. Tyler L. Hayes
5. K. J. Joseph
6. Gyuhak Kim
7. Yaoyao Liu
8. Da-Wei Zhou
9. Arthur Douillard
10. Alan R. Wagner

Papers from Nicolás Guil Mata's perspective

Ordered by how strongly the researchers Nicolás Guil Mata trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Learning Multiple Layers of Features from Tiny Images
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet Large Scale Visual Recognition Challenge
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• End-to-End Incremental Learning (2018) — 1,337 citations
• iLGaCo: Incremental Learning of Gait Covariate Factors (2020) — 7 citations