David Marecek

Natural Language Processing · 2011–2020

A researcher in the Science corpus: 8 papers; trusts 88 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 88 citations into it
• Artificial Intelligence — 75 citations into it; #32,238 of 35,345 by trust here, #17,412 by citations
• Evaluation & Benchmarks — 20 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Machine Translation — 8 citations into it
• Model Architectures — 7 citations into it
• Large Language Models — 5 citations into it

Whom David Marecek trusts overall

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

1. Philipp Koehn
2. Mitchell P. Marcus
3. Christopher D. Manning
4. Rico Sennrich
5. Franz Joseph Och
6. Yoshua Bengio
7. Martin Popel
8. Ashish Vaswani
9. Alexis Conneau
10. Geoffrey E. Hinton

Who trusts David Marecek overall

1. Yonatan Belinkov
2. Zhiyong Wu
3. K. McKeown
4. Yun Chen
5. Taeuk Kim
6. Ondrej Bojar
7. Alessandro Raganato
8. Benjamin Hoover
9. Gonçalo M. Correia
10. Kristen Parton

Papers from David Marecek's perspective

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

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

Their papers

• Are Multilingual Neural Machine Translation Models Better at Capturing Linguistic Features? (2020) — 10 citations
• Extracting Syntactic Trees from Transformer Encoder Self-Attentions (2018) — 37 citations
• Adaptation of machine translation for multilingual information retrieval in the medical domain (2014) — 50 citations
• From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions (2019) — 57 citations
• Inducing Syntactic Trees from BERT Representations (2019) — 24 citations
• Universal Dependencies 2.1 (2017) — 425 citations
• Merged bilingual trees based on Universal Dependencies in Machine Translation (2016) — 3 citations
• Two-step translation with grammatical post-processing (2011) — 40 citations