Angelina McMillan-Major
Artificial Intelligence · 2021–2023
A researcher in the Science corpus: 9 papers; trusts 48 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 460 citations into it; #14,037 of 35,345 by trust here, #1,654 by citations
- Natural Language Processing — 356 citations into it
- Evaluation & Benchmarks — 225 citations into it
- Computer Vision and Pattern Recognition — 125 citations into it
- Large Language Models — 110 citations into it; #353 of 799 by trust here, #145 by citations
- Safety, Robustness & Interpretability — 70 citations into it; #1,158 of 2,251 by trust here, #174 by citations
- Safety Research — 42 citations into it
- Efficiency & Compression — 27 citations into it
Whom Angelina McMillan-Major trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Emily M. Bender
- Geoffrey E. Hinton
- Yoshua Bengio
- Timnit Gebru
- Ilya Sutskever
- Diederik P. Kingma
- Alex Krizhevsky
- Tomas Mikolov
- Samuel R. Bowman
- Yann LeCun
Who trusts Angelina McMillan-Major overall
- Sebastian Gehrmann
- Christopher Akiki
- Emilio Ferrara
- David Rozado
- Stella Biderman
- Murray Shanahan
- Sachin Kumar
Papers from Angelina McMillan-Major's perspective
Ordered by how strongly the researchers Angelina McMillan-Major trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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
- Learning Multiple Layers of Features from Tiny Images
Their papers
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (2021) — 7,969 citations
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
- GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
- The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023) — 217 citations
- Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards (2021) — 58 citations
- Datasets: A Community Library for Natural Language Processing (2021) — 775 citations
- Measuring Data (2022) — 21 citations
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
- Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources (2022) — 14 citations