Thomas Müller

Artificial Intelligence · with Eisenschlos · 2013–2021

A researcher in the Science corpus: 3 papers; trusts 77 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #17,267 of 35,345 by trust here, #25,040 by citations
• Natural Language Processing — 54 citations into it; #2,757 of 5,688 by trust here, #2,529 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Model Architectures — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it; #2,774 of 3,147 by trust here, #1,817 by citations
• Large Language Models — 9 citations into it
• Information Systems — 8 citations into it

Whom Thomas Müller trusts overall

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

1. Adwait Ratnaparkhi
2. Alexander S. Yeh
3. Geoffrey E. Hinton
4. Michael Collins
5. Yoshua Bengio
6. Diederik P. Kingma
7. Christopher D. Manning
8. Ilya Sutskever
9. Kristina Toutanova
10. Alex Krizhevsky

Who trusts Thomas Müller overall

1. Dat Quoc Nguyen
2. Dario Stojanovski
3. Miguel Ballesteros
4. Chris Dyer
5. Jörg Tiedemann
6. Aarne Talman
7. Alexander M. Fraser
8. Karin M. Verspoor
9. Wenhu Chen
10. Noah A. Smith

Papers from Thomas Müller's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Efficient Higher-Order CRFs for Morphological Tagging (2013) — 222 citations
• MATE: Multi-view Attention for Table Transformer Efficiency (2021) — 108 citations
• TaPas: Weakly Supervised Table Parsing via Pre-training (2020) — 890 citations