Isaac Triguero

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 32 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #17,577 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Information Systems — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #1,839 of 3,145 by trust here, #2,120 by citations
• Statistical Learning Theory — 9 citations into it
• Statistics and Probability — 9 citations into it
• Control and Systems Engineering — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it; #1,637 of 3,147 by trust here, #2,330 by citations

Whom Isaac Triguero trusts overall

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

1. Salvador Garcia
2. David Yarowsky
3. Thomas M. Cover
4. Mohamed Farouk Abdel Hady
5. Francisco Herrera
6. Jaka Demsar
7. Jesús Alcalá-Fdez
8. Alberto Fernández
9. Yuhai Wu
10. Thorsten Joachims

Who trusts Isaac Triguero overall

1. Jesper E. van Engelen
2. Yuji Roh
3. Ava Vali
4. Ke Mei
5. Jacob Hoxbroe Jeppesen
6. Xin He
7. Meng Wang
8. Qianru Sun
9. Zhiquan Ye
10. Holger H. Hoos

Papers from Isaac Triguero's perspective

Ordered by how strongly the researchers Isaac Triguero 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019) — 360 citations
• kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data (2017) — 309 citations
• Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015) — 586 citations
• On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification (2014) — 85 citations