Gregor Betz
Artificial Intelligence · 2020–2021
A researcher in the Science corpus: 2 papers; trusts 83 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 58 citations into it; #13,505 of 35,345 by trust here, #23,077 by citations
- Natural Language Processing — 44 citations into it
- Large Language Models — 23 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
- Evaluation & Benchmarks — 15 citations into it
- Prompting & Reasoning — 12 citations into it; #91 of 359 by trust here, #315 by citations
- Information Systems — 5 citations into it
Whom Gregor Betz trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Kyle Richardson
- Alon Talmor
- Geoffrey E. Hinton
- Nora Kassner
- Peter Clark
- Diederik P. Kingma
- Ilya Sutskever
- Alec Radford
- Alex Krizhevsky
Who trusts Gregor Betz overall
- Abulhair Saparov
- Chadi Helwe
- Kaj Bostrom
- Nils Knoth
- Antonia Creswell
- He He
- Ofir Press
- Chloé Clavel
- Xinyu Zhao
- Antonia Tolzin
Papers from Gregor Betz's perspective
Ordered by how strongly the researchers Gregor Betz trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2 (2021) — 34 citations
- Critical Thinking for Language Models (2020) — 44 citations