José Manuel Benítez

Artificial Intelligence · 1997–2016

A researcher in the Science corpus: 6 papers; trusts 37 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #21,139 of 35,345 by trust here, #18,586 by citations
• Molecular Biology — 37 citations into it; #17,168 of 17,773 by trust here, #13,563 by citations
• Information Systems — 24 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Statistics and Probability — 15 citations into it
• Electrical and Electronic Engineering — 14 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 10 citations into it

Whom José Manuel Benítez trusts overall

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

1. Martin A. Riedmiller
2. Stephen Grossberg
3. Michael I. Jordan
4. Teuvo Kohonen
5. Jeffrey L. Elman
6. Yann LeCun
7. Martin Fodslette Møller
8. Frauke Günther
9. Alex J. Cannon
10. Muthu Dayalan

Who trusts José Manuel Benítez overall

1. Jan Ruben Zilke
2. G. Zhang
3. Ron Wehrens
4. Christoph Bergmeir
5. Vítor Cerqueira
6. Eneldo Loza Mencía
7. Bartosz Krawczyk
8. Xiaodong Li
9. Leilani H. Gilpin
10. Johannes W. Kruisselbrink

Papers from José Manuel Benítez's perspective

Ordered by how strongly the researchers José Manuel Benítez 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• On the use of cross-validation for time series predictor evaluation (2012) — 1,121 citations
• A review of microarray datasets and applied feature selection methods (2014) — 638 citations
• Big data preprocessing: methods and prospects (2016) — 594 citations
• On the use of MapReduce for imbalanced big data using Random Forest (2014) — 317 citations
• Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS (2012) — 313 citations
• Are artificial neural networks black boxes? (1997)