Ariel Herbert-Voss

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 6 papers; trusts 4 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 341 citations into it; #7,838 of 35,345 by trust here, #119 by citations

Whom Ariel Herbert-Voss trusts

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

1. Ilya Sutskever (in Artificial Intelligence)
2. Yoshua Bengio (in Artificial Intelligence)
3. Quoc V. Le (in Artificial Intelligence)
4. Diederik P. Kingma (in Artificial Intelligence)

Who trusts Ariel Herbert-Voss

1. Luke S. Zettlemoyer (in Artificial Intelligence)
2. Yejin Choi (in Artificial Intelligence)
3. Noah A. Smith (in Artificial Intelligence)
4. Graham Neubig (in Artificial Intelligence)
5. Hannaneh Hajishirzi (in Artificial Intelligence)
6. Percy Liang (in Artificial Intelligence)
7. Jianfeng Gao (in Artificial Intelligence)
8. Mohit Bansal (in Artificial Intelligence)
9. Michael Lewis (in Artificial Intelligence)
10. Quoc V. Le (in Artificial Intelligence)

Papers from Ariel Herbert-Voss's perspective

Ordered by how strongly the researchers Ariel Herbert-Voss trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
4. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
5. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
6. Language Models are Unsupervised Multitask Learners
7. ImageNet classification with deep convolutional neural networks
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. 5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
10. Generating Sequences With Recurrent Neural Networks

Their papers

• Language Models are Few-Shot Learners (2020) — 61,005 citations
• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• Extracting Training Data from Large Language Models (2020) — 3,168 citations
• Release Strategies and the Social Impacts of Language Models (2019) — 880 citations
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020) — 480 citations
• The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning (2024) — 473 citations