Kaixuan Ji

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it
• Large Language Models — 49 citations into it
• Natural Language Processing — 43 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Prompting & Reasoning — 31 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Kaixuan Ji trusts overall

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

1. Yoshua Bengio
2. Xavier Carreras
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Xiang Lisa Li
6. Alec Radford
7. Alex Krizhevsky
8. Ilya Sutskever
9. Percy Liang
10. Yann LeCun

Who trusts Kaixuan Ji overall

1. Renrui Zhang
2. Jingfeng Yang

Papers from Kaixuan Ji's perspective

Ordered by how strongly the researchers Kaixuan Ji trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks (2021) — 1,125 citations
• P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022) — 1,117 citations
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models (2024) — 604 citations