J. Edward Hu

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 207 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 274 citations into it; #24,652 of 35,345 by trust here, #2,545 by citations
• Natural Language Processing — 161 citations into it
• Computer Vision and Pattern Recognition — 95 citations into it
• Optimization & Training — 47 citations into it
• Evaluation & Benchmarks — 45 citations into it
• Model Architectures — 38 citations into it
• Scaling & Learning Theory — 35 citations into it
• Statistical and Nonlinear Physics — 28 citations into it

Whom J. Edward Hu trusts overall

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

1. Greg Yang
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Krizhevsky
7. Tomas Mikolov
8. Kaiming He
9. Yann LeCun
10. Alex Graves

Who trusts J. Edward Hu overall

1. Renrui Zhang
2. Yi-Lin Sung
3. Jordan Clive
4. Hong-Liu Cao
5. Moo Jin Kim
6. Nathaniel Weir
7. Minghua Liu
8. Joel Jang
9. Bo Li
10. Zhiqing Sun

Papers from J. Edward Hu's perspective

Ordered by how strongly the researchers J. Edward Hu 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• LoRA: Low-Rank Adaptation of Large Language Models (2021) — 21,145 citations
• Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering (2019) — 70 citations
• Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (2018) — 10 citations
• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (2022) — 295 citations
• GFlowNet Foundations (2021) — 363 citations
• Feature Learning in Infinite-Width Neural Networks (2020) — 209 citations
• Guided Generation of Cause and Effect (2020) — 89 citations
• Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations (2018) — 12 citations