Sven Buechel
Artificial Intelligence · 2016–2020
A researcher in the Science corpus: 5 papers; trusts 88 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 65 citations into it; #13,689 of 35,345 by trust here, #19,439 by citations
- Natural Language Processing — 26 citations into it; #3,092 of 5,688 by trust here, #4,361 by citations
- Representation & Self-Supervised Learning — 16 citations into it
- Experimental and Cognitive Psychology — 11 citations into it; #4,269 of 9,731 by trust here, #5,729 by citations
- Cognitive Science & Psychology — 8 citations into it
- Evaluation & Benchmarks — 7 citations into it; #1,920 of 3,147 by trust here, #2,971 by citations
- Social Psychology — 6 citations into it
- Information Systems — 5 citations into it
Whom Sven Buechel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Carlo Strapparava
- Diederik P. Kingma
- Udo Hahn
- Saif M. Mohammad
- Geoffrey E. Hinton
- Tomas Mikolov
- Piotr Bojanowski
- Jimmy Ba
- Yoshua Bengio
- Paul Ekman
Who trusts Sven Buechel overall
- Udo Hahn
- Susanna Rücker
- Jwala Dhamala
- Anneke Buffone
- Tony Sun
- Alex Wang
- Yada Pruksachatkun
- Barry Slaff
- Ashish Sharma
- Aida Mostafazadeh Davani
Papers from Sven Buechel's perspective
Ordered by how strongly the researchers Sven Buechel trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Modeling Empathy and Distress in Reaction to News Stories (2018) — 152 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