Sebastian Schuster

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 9 papers; trusts 120 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #22,149 of 35,345 by trust here, #9,760 by citations
• Natural Language Processing — 90 citations into it; #3,224 of 5,688 by trust here, #1,660 by citations
• Evaluation & Benchmarks — 24 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #20,390 of 21,575 by trust here, #11,286 by citations
• Large Language Models — 11 citations into it
• Molecular Biology — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Sebastian Schuster trusts overall

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

1. Philipp Koehn
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Jacob Devlin
7. Christopher D. Manning
8. Alex Krizhevsky
9. Richard Socher
10. Kaiming He

Who trusts Sebastian Schuster overall

1. Jiayuan Mao
2. Genta Indra Winata
3. Sean MacAvaney
4. Yinhe Zheng
5. Justin Johnson
6. Asli Celikyilmaz
7. Faisal Ladhak
8. Xu Yang
9. Andrea Madotto
10. Md. Zakir Hossain

Papers from Sebastian Schuster's perspective

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

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

Their papers

• Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval (2015) — 372 citations
• Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020) — 657 citations
• Human Effort and Machine Learnability in Computer Aided Translation (2014) — 72 citations
• Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (2018) — 321 citations
• CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017) — 354 citations
• When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it (2022) — 30 citations
• Universal Dependencies 2.1 (2017) — 425 citations
• Proceedings of the Ninth Workshop on Statistical Machine Translation (2014) — 27 citations
• Edinburgh Research Explorer Stanford University's Submissions to the WMT 2014 Translation Task (2014)