Eneldo Loza Mencía

Artificial Intelligence · 2008–2017

A researcher in the Science corpus: 6 papers; trusts 64 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #10,138 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Information Systems — 19 citations into it
• Natural Language Processing — 15 citations into it
• Safety, Robustness & Interpretability — 13 citations into it; #599 of 2,251 by trust here, #1,650 by citations
• Optimization & Training — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Eneldo Loza Mencía trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Tomas Mikolov
8. Thorsten Joachims
9. Jimmy Ba
10. Alex Graves

Who trusts Eneldo Loza Mencía overall

1. Leilani H. Gilpin
2. Min-Ling Zhang
3. Guido Bologna
4. Weiwei Liu
5. Gesina Schwalbe
6. Mark T. Keane
7. Nadia Burkart
8. Vaishak Belle
9. Jack Lanchantin
10. Francisco Charte

Papers from Eneldo Loza Mencía's perspective

Ordered by how strongly the researchers Eneldo Loza Mencía 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Large-Scale Multi-label Text Classification - Revisiting Neural Networks (2013) — 384 citations
• Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification (2017) — 184 citations
• Using semantic similarity for multi-label zero-shot classification of text documents (2016) — 54 citations
• DeepRED - Rule Extraction from Deep Neural Networks (2016) — 236 citations
• Extracting Rules from Deep Neural Networks (2015)
• Multilabel classification via calibrated label ranking (2008) — 953 citations