David Martínez-Rubio

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 47 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #32,014 of 35,345 by trust here, #13,962 by citations
• Optimization & Training — 20 citations into it; #2,458 of 2,601 by trust here, #1,537 by citations
• Computer Vision and Pattern Recognition — 8 citations into it; #19,672 of 21,575 by trust here, #9,563 by citations
• Model Architectures — 8 citations into it
• Computational Mechanics — 7 citations into it; #9,008 of 10,001 by trust here, #3,559 by citations

Whom David Martínez-Rubio trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Nicholas John Higham
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Sepp Hochreiter
7. John C. Duchi
8. Yann LeCun
9. Jonathan H. Manton
10. Jimmy Ba

Who trusts David Martínez-Rubio overall

1. Weiyang Liu
2. Sangho Lee
3. Xuanyi Dong
4. Jerry Ma

Papers from David Martínez-Rubio's perspective

Ordered by how strongly the researchers David Martínez-Rubio 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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

• Online Learning Rate Adaptation with Hypergradient Descent (2017) — 284 citations
• Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group (2019) — 251 citations