Andy Coenen
Artificial Intelligence · 2019–2024
A researcher in the Science corpus: 9 papers; trusts 50 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 171 citations into it; #34,286 of 35,345 by trust here, #6,330 by citations
- Natural Language Processing — 117 citations into it; #5,475 of 5,688 by trust here, #2,011 by citations
- Computer Vision and Pattern Recognition — 64 citations into it
- Large Language Models — 38 citations into it; #757 of 799 by trust here, #553 by citations
- Information Systems — 18 citations into it
- Prompting & Reasoning — 15 citations into it
- Generative Models — 12 citations into it
Whom Andy Coenen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Diederik P. Kingma
- Yann LeCun
- Jacob Devlin
- Christopher D. Manning
- Ashish Vaswani
- Tomas Mikolov
Who trusts Andy Coenen overall
- Chulhee Yun
- Michael A. Lepori
- Julian Michael
- Ethan A. Chi
- Aditya Kanade
- R. Thomas McCoy
- Jesse Vig
- Thomas Scialom
- Xipeng Qiu
- Marta Ruiz Costa-jussà
Papers from Andy Coenen's perspective
Ordered by how strongly the researchers Andy Coenen trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Visualizing and Measuring the Geometry of BERT (2019) — 480 citations
- The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020) — 224 citations
- SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets (2021) — 25 citations
- A Recipe for Arbitrary Text Style Transfer with Large Language Models (2021) — 158 citations
- Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
- Wordcraft: Story Writing With Large Language Models (2022) — 460 citations
- SynthBio: A Case Study in Faster Curation of Text Datasets (2021) — 16 citations
- Wordcraft: a Human-AI Collaborative Editor for Story Writing (2021) — 94 citations
- Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models (2020) — 326 citations