Javier M. Moguerza

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 3 papers; trusts 43 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Statistical Learning Theory — 18 citations into it
• Statistics and Probability — 16 citations into it
• Control and Systems Engineering — 9 citations into it
• Analytical Chemistry — 7 citations into it
• Optimization & Training — 7 citations into it
• Signal Processing — 6 citations into it

Whom Javier M. Moguerza trusts overall

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

1. Bernhard Schölkopf
2. Leo Breiman
3. Shun-ichi Amari
4. Vladimir Naumovich Vapnik
5. John C. Platt
6. Thorsten Joachims
7. Sujun Hua
8. Tong Zhang
9. Alex Smola
10. Ralf Herbrich

Who trusts Javier M. Moguerza overall

1. Emilio Carrizosa

Papers from Javier M. Moguerza's perspective

Ordered by how strongly the researchers Javier M. Moguerza 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Random forest explainability using counterfactual sets (2020) — 79 citations
• A review of machine learning kernel methods in statistical process monitoring (2020) — 102 citations
• Support Vector Machines with Applications (2006) — 354 citations