Rishi Bommasani
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 6 papers; trusts 144 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 329 citations into it
- Natural Language Processing — 201 citations into it; #4,101 of 5,688 by trust here, #5,296 by citations
- Large Language Models — 121 citations into it
- Evaluation & Benchmarks — 111 citations into it; #2,192 of 3,147 by trust here, #928 by citations
- Computer Vision and Pattern Recognition — 80 citations into it
- General Social Sciences — 11 citations into it
Whom Rishi Bommasani trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Richard Socher
- David M. Blei
- Christopher D. Manning
- Yann LeCun
- Kaiming He
Who trusts Rishi Bommasani overall
- Sebastian Gehrmann
- Faisal Ladhak
- Jonas Oppenlaender
- Alessandro Lenci
- Elizabeth Clark
- Esin Durmus
- Toufique Ahmed
- Jason Wei
- Peter Henderson
- Thibault Sellam
Papers from Rishi Bommasani's perspective
Ordered by how strongly the researchers Rishi Bommasani trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Gradient-based learning applied to document recognition
- 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
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Emergent Abilities of Large Language Models (2022) — 3,700 citations
- Holistic Evaluation of Language Models (2023) — 1,863 citations
- Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) — 65 citations
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
- Intrinsic Evaluation of Summarization Datasets (2020) — 74 citations