Antonio Jesús Rivera

Artificial Intelligence · 2015–2016

A researcher in the Science corpus: 4 papers; trusts 36 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #20,258 of 35,345 by trust here, #16,771 by citations
• Information Systems — 27 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #19,618 of 21,575 by trust here, #17,258 by citations
• Electrical and Electronic Engineering — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it; #2,725 of 3,147 by trust here, #2,799 by citations
• Molecular Biology — 12 citations into it; #16,649 of 17,773 by trust here, #13,891 by citations
• Statistical Learning Theory — 10 citations into it
• Statistics and Probability — 7 citations into it

Whom Antonio Jesús Rivera trusts overall

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

1. Min-Ling Zhang
2. Grigorios Tsoumakas
3. Andre´ Elisseeff
4. Zhi-Hua Zhou
5. Eyke Hüllermeier
6. Weiwei Cheng
7. Nitesh V. Chawla
8. Johannes Fürnkranz
9. Ioannis P. Vlahavas
10. Jason Aaron Edward Weston

Who trusts Antonio Jesús Rivera overall

1. Salvador Garcia
2. Francisco Charte
3. Francisco Herrera
4. Harsurinder Kaur
5. Min-Ling Zhang
6. Alberto Fernández
7. Sergio Ramírez-Gallego

Papers from Antonio Jesús Rivera's perspective

Ordered by how strongly the researchers Antonio Jesús Rivera 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Multilabel Classification (2016) — 242 citations
• Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015) — 294 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