Christian Buck

Artificial Intelligence · 2013–2021

A researcher in the Science corpus: 8 papers; trusts 80 researchers and is trusted by 86 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #4,222 of 35,345 by trust here, #3,609 by citations
• Natural Language Processing — 154 citations into it; #747 of 5,688 by trust here, #675 by citations
• Evaluation & Benchmarks — 71 citations into it; #450 of 3,147 by trust here, #847 by citations
• Computer Vision and Pattern Recognition — 41 citations into it
• Reinforcement Learning — 26 citations into it
• Information Systems — 24 citations into it; #7,911 of 8,771 by trust here, #2,471 by citations
• Management Science and Operations Research — 10 citations into it
• Large Language Models — 9 citations into it

Whom Christian Buck trusts overall

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

1. Philipp Koehn
2. Ronald J. Williams
3. Yoshua Bengio
4. Chris Callison-Burch
5. Ilya Sutskever
6. Geoffrey E. Hinton
7. Dzmitry Bahdanau
8. Diederik P. Kingma
9. Robert C. Moore
10. Nadir Durrani

Who trusts Christian Buck overall

1. Philipp Koehn
2. Ondrej Bojar
3. Edouard Grave
4. Varvara Logacheva
5. Holger Schwenk
6. Rodrigo Nogueira
7. Vishrav Chaudhary
8. Ahmed El-Kishky
9. Yvette Graham
10. Lucia Specia

Papers from Christian Buck's perspective

Ordered by how strongly the researchers Christian Buck 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• Findings of the 2014 Workshop on Statistical Machine Translation (2014) — 850 citations
• N-gram Counts and Language Models from the Common Crawl (2014) — 194 citations
• Ask the Right Questions: Active Question Reformulation with Reinforcement Learning (2017) — 177 citations
• Boosting Search Engines with Interactive Agents (2021) — 24 citations
• Findings of the WMT 2016 Bilingual Document Alignment Shared Task (2016) — 54 citations
• FBK-UPV-UEdin participation in the WMT14 Quality Estimation shared-task (2014) — 53 citations
• FBK-UEdin Participation to the WMT13 Quality Estimation Shared Task (2013) — 39 citations
• Findings of the 2013 Workshop on Statistical Machine Translation (2013) — 387 citations