Jose Garcia Moreno-Torres

Artificial Intelligence · 2012

A researcher in the Science corpus: 3 papers; trusts 24 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #16,001 of 35,345 by trust here, #9,465 by citations
• Information Systems — 15 citations into it
• Statistics and Probability — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Statistical Learning Theory — 9 citations into it; #1,013 of 1,038 by trust here, #424 by citations
• Computer Networks and Communications — 7 citations into it
• Accounting — 6 citations into it

Whom Jose Garcia Moreno-Torres trusts overall

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

1. Masashi Sugiyama
2. Salvador Garcia
3. J. Ross Quinlan
4. Bianca Zadrozny
5. Charles Peter Elkan
6. Joaquin Quionero-Candela
7. Battista Biggio
8. Joaquin Quiñonero-Candela
9. Yanmin Sun
10. Lotfi A. Zadeh

Who trusts Jose Garcia Moreno-Torres overall

1. Octavio Loyola-González
2. Mingxia Liu
3. Verónica Bolón-Canedo
4. Christoph Bergmeir
5. Wouter M. Kouw
6. Kaiyang Zhou
7. Manuel Eugenio Morocho-Cayamcela
8. Alberto Fernández
9. Noelia Sánchez-Maroño
10. Raúl Monroy

Papers from Jose Garcia Moreno-Torres's perspective

Ordered by how strongly the researchers Jose Garcia Moreno-Torres 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. Support-Vector Networks
6. Statistical Learning Theory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Long Short-Term Memory

Their papers

• A unifying view on dataset shift in classification (2012) — 1,155 citations
• Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012) — 312 citations
• Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation (2012) — 313 citations