Huifeng Guo

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 5 papers; trusts 20 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #21,149 of 35,345 by trust here, #8,706 by citations
• Information Systems — 75 citations into it; #4,490 of 8,771 by trust here, #1,086 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #13,241 of 21,575 by trust here, #6,737 by citations
• Recommender Systems — 21 citations into it; #154 of 233 by trust here, #80 by citations
• Model Architectures — 12 citations into it
• Graph Learning — 9 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Huifeng Guo trusts overall

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

1. Steffen Rendle
2. Taher H. Haveliwala
3. Geoffrey E. Hinton
4. Aäron van den Oord
5. Yoshua Bengio
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Kaiming He
9. Ruslan Salakhutdinov
10. Ilya Sutskever

Who trusts Huifeng Guo overall

1. Chuhan Wu
2. Fangzhao Wu
3. Le Wu
4. Fajie Yuan
5. Bin Liu
6. Mingxiao An
7. Aminu Da’u
8. Xiangnan He
9. Xing Xie
10. Chen Gao

Papers from Huifeng Guo's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017) — 3,129 citations
• How Can Recommender Systems Benefit from Large Language Models: A Survey (2023) — 404 citations
• AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction (2020) — 60 citations
• GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (2020) — 110 citations
• A Graph-Based Push Service Platform (2016) — 6 citations