Oscar Täckström

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 7 papers; trusts 400 researchers and is trusted by 229 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #1,736 of 35,345 by trust here, #2,944 by citations
• Natural Language Processing — 107 citations into it; #753 of 5,688 by trust here, #768 by citations
• Computer Vision and Pattern Recognition — 65 citations into it; #2,895 of 21,575 by trust here, #2,401 by citations
• Evaluation & Benchmarks — 19 citations into it
• Generative Models — 18 citations into it; #757 of 1,956 by trust here, #1,068 by citations
• Model Architectures — 16 citations into it
• Information Extraction — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Oscar Täckström trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Xavier Carreras
7. Yann LeCun
8. Kaiming He
9. Christopher D. Manning
10. Alex Graves

Who trusts Oscar Täckström overall

1. Bishan Yang
2. Swabha Swayamdipta
3. Luheng He
4. Kenton C. T. Lee
5. Sunipa Dev
6. Yi Tay
7. Luke S. Zettlemoyer
8. James Thorne
9. Yoav Goldberg
10. David Belanger

Papers from Oscar Täckström's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A Decomposable Attention Model for Natural Language Inference (2016) — 1,424 citations
• Scaling Autoregressive Video Models (2019) — 256 citations
• Semantic Role Labeling with Neural Network Factors (2015) — 152 citations
• Efficient Inference and Structured Learning for Semantic Role Labeling (2015) — 92 citations
• Transforming Dependency Structures to Logical Forms for Semantic Parsing (2016) — 172 citations
• Neural Paraphrase Identification of Questions with Noisy Pretraining (2017) — 81 citations
• Universal Dependency Annotation for Multilingual Parsing (2013) — 603 citations