J. A. Carrasco-Ochoa
Artificial Intelligence · 2010–2019
A researcher in the Science corpus: 10 papers; trusts 50 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 114 citations into it; #11,231 of 35,345 by trust here, #12,083 by citations
- Computer Vision and Pattern Recognition — 38 citations into it
- Information Systems — 35 citations into it; #4,261 of 8,771 by trust here, #3,098 by citations
- Evaluation & Benchmarks — 23 citations into it; #2,698 of 3,147 by trust here, #2,482 by citations
- Statistics and Probability — 19 citations into it
- Computational Theory and Mathematics — 18 citations into it; #10,142 of 12,182 by trust here, #11,462 by citations
- Optimization & Training — 18 citations into it
- Electrical and Electronic Engineering — 16 citations into it; #10,446 of 16,539 by trust here, #11,313 by citations
Whom J. A. Carrasco-Ochoa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jaka Demsar
- Huan Liu
- Robert Tibshirani
- Zheng Zhao
- Corinna Cortes
- J. Ross Quinlan
- Vladimir Naumovich Vapni
- Thomas G. Dietterich
- Xiaofei He
- Vladimir Naumovich Vapnik
Who trusts J. A. Carrasco-Ochoa overall
- Octavio Loyola-González
- Riccardo Guidotti
- Terrance Devries
- Tomás Borovicka
- Reihaneh H. Hariri
- Arman Malekloo
- Raúl Monroy
- George Cazenavette
Papers from J. A. Carrasco-Ochoa's perspective
Ordered by how strongly the researchers J. A. Carrasco-Ochoa trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- A review of instance selection methods (2010) — 401 citations
- Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases (2016) — 173 citations
- PBC4cip: A new contrast pattern-based classifier for class imbalance problems (2017) — 73 citations
- Fuzzy emerging patterns for classifying hard domains (2011) — 39 citations
- A Pattern-Based Approach for Detecting Pneumatic Failures on Temporary Immersion Bioreactors (2019) — 12 citations
- A review of unsupervised feature selection methods (2019) — 553 citations
- Effect of class imbalance on quality measures for contrast patterns: An experimental study (2016) — 17 citations
- Detecting Pneumatic Failures on Temporary Immersion Bioreactors (2016) — 3 citations
- Finding the best diversity generation procedures for mining contrast patterns (2015) — 31 citations
- A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification (2010) — 18 citations