Jesse Mu

Artificial Intelligence · with Goodman · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 452 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #12,928 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #19,923 of 21,575 by trust here, #16,367 by citations
• Natural Language Processing — 60 citations into it
• Reinforcement Learning — 42 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Model Architectures — 18 citations into it
• Large Language Models — 17 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #2,897 of 3,145 by trust here, #2,757 by citations

Whom Jesse Mu trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Karen Simonyan
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Jesse Mu overall

1. Andrew Kyle Lampinen
2. Leonard Bereska
3. Andreas Madsen
4. Stephen Casper
5. Catherine Wong
6. Eric Zelikman
7. Joe Stacey
8. Efstratios Gavves
9. Bill Yuchen Lin
10. Siva Reddy

Papers from Jesse Mu's perspective

Ordered by how strongly the researchers Jesse Mu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Shaping Visual Representations with Language for Few-Shot Classification (2019) — 49 citations
• Improving Intrinsic Exploration with Language Abstractions (2022) — 78 citations
• Compositional Explanations of Neurons (2020) — 225 citations
• Learning to Compress Prompts with Gist Tokens (2023) — 409 citations
• Emergent Communication of Generalizations (2021) — 56 citations