Yao Fu
Artificial Intelligence · with Khot · 2021–2023
A researcher in the Science corpus: 7 papers; trusts 76 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 236 citations into it; #32,175 of 35,345 by trust here, #4,722 by citations
- Large Language Models — 98 citations into it
- Natural Language Processing — 88 citations into it
- Prompting & Reasoning — 74 citations into it; #277 of 359 by trust here, #158 by citations
- Computer Vision and Pattern Recognition — 55 citations into it
- Computer Networks and Communications — 7 citations into it
Whom Yao Fu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jason Wei
- Geoffrey E. Hinton
- Xuezhi Wang
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Quoc V. Le
Who trusts Yao Fu overall
- Jie Huang
- Hui Yang
- L. Yu
- Pan Lu
- Zhuosheng Zhang
- Guohao Li
- Wayne Xin Zhao
- Junlin Wang
- Junjie Zhang
- Zheng Yuan
Papers from Yao Fu's perspective
Ordered by how strongly the researchers Yao Fu trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks (2022) — 735 citations
- Complexity-Based Prompting for Multi-Step Reasoning (2022) — 629 citations
- MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning (2023) — 596 citations
- C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models (2023) — 890 citations
- Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance (2023) — 134 citations
- Specializing Smaller Language Models towards Multi-Step Reasoning (2023) — 363 citations
- Prototypical Representation Learning for Relation Extraction (2021) — 67 citations