M. J. Jesús
Artificial Intelligence · 2008–2019
A researcher in the Science corpus: 10 papers; trusts 139 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 218 citations into it; #9,802 of 35,345 by trust here, #6,103 by citations
- Computer Vision and Pattern Recognition — 89 citations into it; #18,558 of 21,575 by trust here, #15,529 by citations
- Information Systems — 63 citations into it
- Signal Processing — 35 citations into it
- Representation & Self-Supervised Learning — 32 citations into it
- Electrical and Electronic Engineering — 26 citations into it; #14,159 of 16,539 by trust here, #10,714 by citations
- Optimization & Training — 23 citations into it
- Evaluation & Benchmarks — 22 citations into it; #2,727 of 3,147 by trust here, #2,799 by citations
Whom M. J. Jesús trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Min-Ling Zhang
- Yann LeCun
- Diederik P. Kingma
- Grigorios Tsoumakas
- Francisco Herrera
- Alex Krizhevsky
- Nitesh V. Chawla
- Hisao Ishibuchi
Who trusts M. J. Jesús overall
- Salvador Garcia
- Francisco Herrera
- Alberto Fernández
- Victoria López
- Francisco Charte
- Paula Branco
- Bartosz Krawczyk
- Mikel Galar
- Julián Luengo
Papers from M. J. Jesús's perspective
Ordered by how strongly the researchers M. J. Jesús 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Multilabel Classification (2016) — 242 citations
- An overview on subgroup discovery: foundations and applications (2011) — 317 citations
- Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? (2019) — 189 citations
- Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013) — 390 citations
- Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015) — 294 citations
- A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018) — 278 citations
- MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation (2015) — 246 citations
- QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015) — 30 citations
- KEEL: a software tool to assess evolutionary algorithms for data mining problems (2008) — 1,515 citations
- A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008) — 323 citations