Daniel Dahlmeier

Artificial Intelligence · 2010–2017

A researcher in the Science corpus: 6 papers; trusts 94 researchers and is trusted by 68 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #16,498 of 35,345 by trust here, #12,387 by citations
• Natural Language Processing — 48 citations into it; #723 of 5,688 by trust here, #2,307 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Model Architectures — 9 citations into it
• Information Systems — 6 citations into it

Whom Daniel Dahlmeier trusts overall

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

1. Yoshua Bengio
2. Richard Socher
3. Geoffrey E. Hinton
4. Tomas Mikolov
5. Daniel Gildea
6. Diederik P. Kingma
7. Mitchell P. Marcus
8. Ilya Sutskever
9. Alex Graves
10. Minqing Hu

Who trusts Daniel Dahlmeier overall

1. Tao Ge
2. Hwee Tou Ng
3. Mariano Felice
4. Furu Wei
5. Christopher Bryant
6. Shamil Chollampatt
7. Roman Grundkiewicz
8. Joel R. Tetreault
9. Zhaohong Wan
10. Adriane Boyd

Papers from Daniel Dahlmeier's perspective

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

Their papers

• Domain adaptation for semantic role labeling in the biomedical domain (2010) — 75 citations
• Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms (2017) — 410 citations
• An Unsupervised Neural Attention Model for Aspect Extraction (2017) — 393 citations
• Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis (2016) — 387 citations
• Building a Large Annotated Corpus of Learner English: The NUS Corpus of Learner English (2013) — 402 citations
• Better Evaluation for Grammatical Error Correction (2012) — 408 citations