Ulme Wennberg

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 77 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #12,718 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 31 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it; #15,209 of 21,575 by trust here, #21,424 by citations
• Large Language Models — 11 citations into it
• Model Architectures — 10 citations into it; #1,637 of 2,278 by trust here, #2,268 by citations
• Evaluation & Benchmarks — 7 citations into it

Whom Ulme Wennberg trusts overall

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

1. Geoffrey E. Hinton
2. Jacob Devlin
3. Yoshua Bengio
4. Diederik P. Kingma
5. Matthew E. Peters
6. Ashish Vaswani
7. Ilya Sutskever
8. Alex Krizhevsky
9. Quoc V. Le
10. Kaiming He

Who trusts Ulme Wennberg overall

1. Ofir Press
2. Sijia Wang
3. Noah A. Smith
4. Xinya Du
5. Zexuan Zhong
6. Markus Eberts
7. Heng Ji
8. Pengfei Yu
9. Michael Lewis
10. Mo Yu

Papers from Ulme Wennberg's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
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. 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. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Gradient-based learning applied to document recognition

Their papers

• Entity, Relation, and Event Extraction with Contextualized Span Representations (2019) — 684 citations
• The Case for Translation-Invariant Self-Attention in Transformer-Based Language Models (2021) — 26 citations