Michael Röder

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it; #10,443 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 17 citations into it; #3,312 of 5,688 by trust here, #4,793 by citations
• Information Systems — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it; #1,776 of 3,147 by trust here, #2,799 by citations
• Information Extraction — 6 citations into it

Whom Michael Röder trusts overall

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

1. Silviu Cucerzan
2. Johannes Hoffart
3. Roberto Navigli
4. Jey Han Lau
5. David M. Blei
6. David Newman
7. Benjamin Snyder
8. Nikolaos Aletras
9. Nadine Steinmetz
10. Pablo N. Mendes

Who trusts Michael Röder overall

1. Nicola De Cao
2. Isar Nejadgholi
3. Shaheen Syed
4. Armen Aghajanyan
5. Laura Perez-Beltrachini
6. Nikolaos Kolitsas
7. José Padarian
8. Renato Faccini
9. Gautier Izacard
10. Johannes M. van Hulst

Papers from Michael Röder's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Gradient-based learning applied to document recognition

Their papers

• GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017) — 95 citations
• N³ - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format (2014) — 94 citations
• Exploring the Space of Topic Coherence Measures (2015) — 2,293 citations