Alexander Wettig

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 5 papers; trusts 182 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it
• Natural Language Processing — 75 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Information Retrieval & Search — 30 citations into it
• Large Language Models — 30 citations into it
• Retrieval-Augmented Generation — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Alexander Wettig trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Richard Socher
8. Jacob Devlin
9. Yann LeCun
10. Alec Radford

Who trusts Alexander Wettig overall

1. Yi Tay
2. Mostafa Dehghani
3. Chun Xia
4. Haejun Lee
5. Vinh Q. Tran
6. Zhengbao Jiang

Papers from Alexander Wettig's perspective

Ordered by how strongly the researchers Alexander Wettig 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. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Should You Mask 15% in Masked Language Modeling? (2022) — 213 citations
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues? (2023) — 3,028 citations
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering (2024) — 1,448 citations
• Adapting Language Models to Compress Contexts (2023) — 364 citations
• Phrase Retrieval Learns Passage Retrieval, Too (2021) — 50 citations