Yaser Al-Onaizan

Natural Language Processing · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 357 researchers and is trusted by 76 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 107 citations into it; #801 of 5,688 by trust here, #1,366 by citations
• Artificial Intelligence — 106 citations into it; #2,930 of 35,345 by trust here, #5,281 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #3,460 of 21,575 by trust here, #3,955 by citations
• Model Architectures — 29 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Large Language Models — 5 citations into it

Whom Yaser Al-Onaizan trusts overall

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

1. Yoshua Bengio
2. Dzmitry Bahdanau
3. Kyunghyun Cho
4. Ilya Sutskever
5. Alex Graves
6. Philipp Koehn
7. Geoffrey E. Hinton
8. Diederik P. Kingma
9. Jimmy Ba
10. Alex Krizhevsky

Who trusts Yaser Al-Onaizan overall

1. Felix Stahlberg
2. Raj Dabre
3. Chenhui Chu
4. Danielle Saunders
5. David Vilar
6. Yun Chen
7. Markus Freitag
8. Philipp Koehn
9. Jiatao Gu
10. Praveen Dakwale

Papers from Yaser Al-Onaizan's perspective

Ordered by how strongly the researchers Yaser Al-Onaizan 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Zero-Resource Translation with Multi-Lingual Neural Machine Translation (2016) — 285 citations
• Temporal Attention Model for Neural Machine Translation (2016) — 63 citations
• Ensemble Distillation for Neural Machine Translation (2017) — 114 citations
• Fast Domain Adaptation for Neural Machine Translation (2016) — 207 citations
• Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions (2020) — 62 citations
• Training Neural Machine Translation to Apply Terminology Constraints (2019) — 191 citations
• Exploring Content Selection in Summarization of Novel Chapters (2020) — 39 citations
• Beam Search Strategies for Neural Machine Translation (2017) — 513 citations