Verónica Bolón-Canedo

Artificial Intelligence · 2013–2024

A researcher in the Science corpus: 8 papers; trusts 45 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #12,400 of 35,345 by trust here, #13,163 by citations
• Molecular Biology — 67 citations into it; #3,604 of 17,773 by trust here, #7,657 by citations
• Computer Vision and Pattern Recognition — 51 citations into it; #12,065 of 21,575 by trust here, #10,010 by citations
• Information Systems — 23 citations into it; #3,652 of 8,771 by trust here, #7,107 by citations
• Evaluation & Benchmarks — 20 citations into it
• Computer Networks and Communications — 11 citations into it
• ML Systems & Hardware — 11 citations into it
• Optimization & Training — 10 citations into it

Whom Verónica Bolón-Canedo trusts overall

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

1. Huan Liu
2. Mark A. Hall
3. Lei Yu
4. Igor Kononenko
5. George Forman
6. Zheng Zhao
7. Manoranjan Dash
8. Noelia Sánchez-Maroño
9. Aixia Guo
10. Muthu Dayalan

Who trusts Verónica Bolón-Canedo overall

1. Noelia Sánchez-Maroño
2. Nicholas Pudjihartono
3. Prachi Agrawal
4. Amparo Alonso-Betanzos
5. Vipin Kumar
6. Tayaza Fadason
7. Hattan F. Abutarboush
8. Haoyue Liu
9. Ryan J. Urbanowicz
10. Tansel Dökeroğlu

Papers from Verónica Bolón-Canedo's perspective

Ordered by how strongly the researchers Verónica Bolón-Canedo 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• A review of feature selection methods on synthetic data (2013) — 836 citations
• A review of microarray datasets and applied feature selection methods (2014) — 638 citations
• Recent advances and emerging challenges of feature selection in the context of big data (2015) — 328 citations
• A review of feature selection methods in medical applications (2019) — 692 citations
• Centralized vs. distributed feature selection methods based on data complexity measures (2017) — 66 citations
• A review of green artificial intelligence: Towards a more sustainable future (2024) — 348 citations
• Ensembles for feature selection: A review and future trends (2019) — 468 citations
• Feature selection for high-dimensional data (2016) — 388 citations