Héctor Martínez Alonso

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 73 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #7,482 of 35,345 by trust here, #20,836 by citations
• Natural Language Processing — 27 citations into it; #1,622 of 5,688 by trust here, #2,440 by citations
• Model Architectures — 16 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #5,689 of 21,575 by trust here, #13,815 by citations
• Evaluation & Benchmarks — 6 citations into it

Whom Héctor Martínez Alonso trusts overall

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

1. Anders Søgaard
2. Yoav Goldberg
3. Miguel Ballesteros
4. Chris Dyer
5. Lingjia Deng
6. Mitchell P. Marcus
7. Zhiheng Huang
8. Barbara Plank
9. Collin F. Baker
10. Hao Cheng

Who trusts Héctor Martínez Alonso overall

1. Sebastian Ruder
2. Isabelle Augenstein
3. Soravit Changpinyo
4. Anders Søgaard
5. Georgios Balikas
6. Nils Reimers
7. Joachim Bingel
8. Dieuwke Hupkes
9. Hexiang Hu
10. Alessandro Raganato

Papers from Héctor Martínez Alonso's perspective

Ordered by how strongly the researchers Héctor Martínez Alonso trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• When is multitask learning effective? Semantic sequence prediction under varying data conditions (2016) — 166 citations
• Universal Dependencies 2.1 (2017) — 425 citations
• Multitask learning for semantic sequence prediction under varying data conditions (2016) — 14 citations