Fajie Yuan

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 8 papers; trusts 215 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 167 citations into it; #18,981 of 35,345 by trust here, #9,968 by citations
• Computer Vision and Pattern Recognition — 91 citations into it; #20,153 of 21,575 by trust here, #8,612 by citations
• Information Systems — 57 citations into it; #3,915 of 8,771 by trust here, #1,672 by citations
• Molecular Biology — 55 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Natural Language Processing — 43 citations into it
• Model Architectures — 33 citations into it
• Large Language Models — 32 citations into it

Whom Fajie Yuan trusts overall

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

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

Who trusts Fajie Yuan overall

1. Xiangnan He
2. Le Wu
3. Yupeng Hou
4. Binzong Geng
5. Tianwen Chen
6. Keqin Bao
7. Jiacheng Li
8. Shoujin Wang
9. Xiang Wang
10. Kun Zhou

Papers from Fajie Yuan's perspective

Ordered by how strongly the researchers Fajie Yuan 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation (2020) — 202 citations
• Exploring evolution-based & -free protein language models as protein function predictors (2022) — 16 citations
• SaProt: Protein Language Modeling with Structure-aware Vocabulary (2024) — 340 citations
• Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited (2023) — 341 citations
• Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification (2021) — 20 citations
• Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking (2021) — 42 citations
• One Person, One Model, One World: Learning Continual User Representation without Forgetting (2020) — 83 citations
• A Simple Convolutional Generative Network for Next Item Recommendation (2018) — 670 citations