J. Dean

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #24,184 of 35,345 by trust here, #2,793 by citations
• Computer Vision and Pattern Recognition — 54 citations into it; #15,184 of 21,575 by trust here, #2,081 by citations
• Natural Language Processing — 37 citations into it
• Model Architectures — 32 citations into it; #1,760 of 2,278 by trust here, #317 by citations
• Large Language Models — 21 citations into it

Whom J. Dean trusts overall

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

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

Who trusts J. Dean overall

1. Noam Shazeer
2. J. Urgen Schmidhuber
3. David R Ha
4. Neil Houlsby

Papers from J. Dean's perspective

Ordered by how strongly the researchers J. Dean 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (2017) — 5,113 citations
• ST-MoE: Designing Stable and Transferable Sparse Expert Models (2022) — 423 citations