Victoria López

Artificial Intelligence · 2012–2014

A researcher in the Science corpus: 5 papers; trusts 61 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #12,225 of 35,345 by trust here, #7,797 by citations
• Information Systems — 36 citations into it; #8,323 of 8,771 by trust here, #7,107 by citations
• Electrical and Electronic Engineering — 32 citations into it; #5,487 of 16,539 by trust here, #4,881 by citations
• Accounting — 18 citations into it; #8,037 of 8,970 by trust here, #5,847 by citations
• Optimization & Training — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Statistical Learning Theory — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Victoria López trusts overall

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

1. Salvador Garcia
2. Nitesh V. Chawla
3. Bianca Zadrozny
4. Charles Peter Elkan
5. J. Ross Quinlan
6. Yanmin Sun
7. Jaka Demsar
8. Gustavo E. A. P. A. Batista
9. Pedro M. Domingos
10. Robert E. Schapire

Who trusts Victoria López overall

1. Bartosz Krawczyk
2. Alberto Fernández
3. Francisco Herrera
4. Salvador Garcia
5. Francisco Charte
6. Abhijit Bhattacharyya
7. Octavio Loyola-González
8. Aisha Abdallah
9. Sheen Mclean Cabaneros
10. Sara del Río

Papers from Victoria López's perspective

Ordered by how strongly the researchers Victoria López trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Going deeper with convolutions
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013) — 1,580 citations
• Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013) — 390 citations
• Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012) — 312 citations
• On the use of MapReduce for imbalanced big data using Random Forest (2014) — 317 citations
• A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning (2013) — 525 citations