Jiaxin Huang

Artificial Intelligence · with Han · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 180 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 242 citations into it; #30,241 of 35,345 by trust here, #9,369 by citations
• Natural Language Processing — 141 citations into it
• Computer Vision and Pattern Recognition — 91 citations into it
• Large Language Models — 70 citations into it; #618 of 799 by trust here, #489 by citations
• Prompting & Reasoning — 66 citations into it
• Evaluation & Benchmarks — 34 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Safety, Robustness & Interpretability — 12 citations into it

Whom Jiaxin Huang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Oriol Vinyals
7. Kaiming He
8. Yann LeCun
9. Jimmy Ba
10. Alex Graves

Who trusts Jiaxin Huang overall

1. Kumar Shridhar
2. Richard Ngo
3. Jie Huang
4. Namgyu Ho
5. Harrison Lee
6. Alessandro Stolfo
7. Robin Jia
8. Omar Khattab
9. Theodore R. Sumers
10. Yao Fu

Papers from Jiaxin Huang's perspective

Ordered by how strongly the researchers Jiaxin Huang 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Large Language Models Can Self-Improve (2022) — 879 citations
• Few-Shot Named Entity Recognition: A Comprehensive Study (2020) — 83 citations
• Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning (2022) — 65 citations
• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding (2022) — 302 citations