Álvaro Peris

Natural Language Processing · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 118 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 54 citations into it; #3,189 of 5,688 by trust here, #4,793 by citations
• Artificial Intelligence — 51 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Model Architectures — 24 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Optimization & Training — 8 citations into it
• Machine Translation — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Álvaro Peris trusts overall

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

1. Alex Graves
2. Yoshua Bengio
3. Diederik P. Kingma
4. Philipp Koehn
5. Razvan Pascanu
6. Sepp Hochreiter
7. Ilya Sutskever
8. Geoffrey E. Hinton
9. Tomas Mikolov
10. Mike Schuster

Who trusts Álvaro Peris overall

1. Amr El-Desoky Mousa
2. Matthew E. Peters
3. Swabha Swayamdipta
4. Björn Schuller
5. Bridger Waleed Ammar
6. Roy Schwartz
7. Marcus Tomalin
8. Jianze Liang
9. Magdalena Biesialska
10. Chandra Bhagavatula

Papers from Álvaro Peris's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Active Learning for Interactive Neural Machine Translation of Data Streams (2018) — 61 citations
• Adapting Neural Machine Translation with Parallel Synthetic Data (2017) — 65 citations
• A Bidirectional Recurrent Neural Language Model for Machine Translation (2015) — 13 citations