Alfredo Vellido

Artificial Intelligence · 2007–2019

A researcher in the Science corpus: 4 papers; trusts 54 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #14,329 of 35,345 by trust here, #28,481 by citations
• Molecular Biology — 19 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Health Information Management — 13 citations into it; #58 of 344 by trust here, #233 by citations
• Medical & Health AI — 13 citations into it
• Safety, Robustness & Interpretability — 13 citations into it; #2,053 of 2,251 by trust here, #1,349 by citations
• Health Informatics — 7 citations into it
• Information Systems — 6 citations into it

Whom Alfredo Vellido trusts overall

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

1. Ian T. Jolliffe
2. Teuvo Kohonen
3. Zhengping Che
4. Shixia Liu
5. Zizhao Zhang
6. Daniele Ravì
7. Michael T. Gastner
8. Geoffrey E. Hinton
9. Finale Doshi-Velez
10. Matthew D. Zeiler

Who trusts Alfredo Vellido overall

1. Dang Lien Minh
2. Mingxia Liu
3. Cynthia Rudin
4. Guolong Su
5. Jessica Taylor
6. H. Xiang Wang
7. Erico Tjoa
8. Benjamin Letham
9. Giulia Vilone
10. John Danaher

Papers from Alfredo Vellido's perspective

Ordered by how strongly the researchers Alfredo Vellido 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. Going deeper with convolutions
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The importance of interpretability and visualization in machine learning for applications in medicine and health care (2019) — 566 citations
• Machine learning in critical care: state-of-the-art and a sepsis case study (2018) — 59 citations
• Making machine learning models interpretable (2012) — 316 citations
• Applying Data Mining Techniques to e-Learning Problems (2007) — 329 citations