Noam Wies

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 84 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #30,239 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 43 citations into it
• Natural Language Processing — 32 citations into it
• Large Language Models — 29 citations into it
• Model Architectures — 29 citations into it
• Scaling & Learning Theory — 15 citations into it
• Prompting & Reasoning — 12 citations into it
• Optimization & Training — 11 citations into it

Whom Noam Wies trusts overall

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

1. Yoshua Bengio
2. Shai Shalev-Shwartz
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Jacob Devlin
6. Gregory Beylkin
7. Yoav Levine
8. Alec Radford
9. Ashish Vaswani
10. Ilya Sutskever

Who trusts Noam Wies overall

1. Behrooz Ghorbani
2. Orhan Firat
3. Yoav Levine
4. Eric Zelikman
5. Markus Freitag
6. Amnon Shashua
7. Ankur Bapna

Papers from Noam Wies's perspective

Ordered by how strongly the researchers Noam Wies 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
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

• Limits to Depth Efficiencies of Self-Attention (2020) — 56 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
• Which transformer architecture fits my data? A vocabulary bottleneck in self-attention (2021)
• The Depth-to-Width Interplay in Self-Attention. (2020) — 33 citations