Udo Hahn

Artificial Intelligence · 2000–2020

A researcher in the Science corpus: 7 papers; trusts 98 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #11,135 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 33 citations into it; #2,630 of 5,688 by trust here, #3,897 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it; #1,919 of 3,147 by trust here, #2,971 by citations
• Experimental and Cognitive Psychology — 10 citations into it; #4,219 of 9,731 by trust here, #5,729 by citations
• Cognitive Science & Psychology — 8 citations into it
• Information Systems — 5 citations into it

Whom Udo Hahn trusts overall

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

1. Carlo Strapparava
2. Sven Buechel
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Andrew McCallum
6. Lawrence R. Rabiner
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ilya Sutskever
10. Burr Settles

Who trusts Udo Hahn overall

1. Sven Buechel
2. Ephraim Nissan
3. Susanna Rücker
4. Burr Settles
5. Ramiz M. Aliguliyev
6. Amir Gandomi
7. Jwala Dhamala
8. Wenjing Han
9. Manuel Lopes

Papers from Udo Hahn's perspective

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

• The Challenges of Automatic Summarization (2000) — 394 citations
• Learning and Evaluating Emotion Lexicons for 91 Languages (2020) — 46 citations
• Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (2018) — 35 citations
• EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis (2017) — 311 citations
• Emotion Analysis as a Regression Problem - Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation (2016) — 82 citations
• Semi-Supervised Active Learning for Sequence Labeling (2009) — 109 citations
• Multi-Task Active Learning for Linguistic Annotations (2008) — 119 citations