Yoav Levine

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 9 papers; trusts 137 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #30,236 of 35,345 by trust here, #10,319 by citations
• Natural Language Processing — 69 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it
• Large Language Models — 50 citations into it
• Model Architectures — 35 citations into it; #1,386 of 2,278 by trust here, #1,794 by citations
• Scaling & Learning Theory — 16 citations into it
• Prompting & Reasoning — 15 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Yoav Levine trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alec Radford
5. Ilya Sutskever
6. Alex Krizhevsky
7. Jacob Devlin
8. Kaiming He
9. Yann LeCun
10. Alex Graves

Who trusts Yoav Levine overall

1. Noam Wies
2. Behrooz Ghorbani
3. Daniel Jannai
4. Amnon Shashua
5. Orhan Firat
6. Katikapalli Subramanyam Kalyan
7. Richard He Bai
8. Shijie Wu
9. Humza Naveed
10. Markus Freitag

Papers from Yoav Levine's perspective

Ordered by how strongly the researchers Yoav Levine trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• In-Context Retrieval-Augmented Language Models (2023) — 1,053 citations
• Limits to Depth Efficiencies of Self-Attention (2020) — 56 citations
• SenseBERT: Driving Some Sense into BERT (2019) — 203 citations
• Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks (2022) — 54 citations
• Which transformer architecture fits my data? A vocabulary bottleneck in self-attention (2021) — 23 citations
• Deep autoregressive models for the efficient variational simulation of many-body quantum systems (2019) — 252 citations
• Limits to Depth Efficiencies of Self-Attention Supplementary Material (2021)
• Which transformer architecture fits my data? A vocabulary bottleneck in self-attention (2021)
• The Depth-to-Width Interplay in Self-Attention. (2020) — 33 citations