Fuzhen Zhuang

Artificial Intelligence · 2010–2026

A researcher in the Science corpus: 15 papers; trusts 548 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 309 citations into it; #32,481 of 35,345 by trust here, #6,157 by citations
• Computer Vision and Pattern Recognition — 172 citations into it
• Representation & Self-Supervised Learning — 144 citations into it
• Information Systems — 121 citations into it
• Recommender Systems — 43 citations into it
• Natural Language Processing — 29 citations into it
• Signal Processing — 28 citations into it
• Evaluation & Benchmarks — 19 citations into it

Whom Fuzhen Zhuang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Kaiming He
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Thorsten Joachims
10. Michael I. Jordan

Who trusts Fuzhen Zhuang overall

1. Mei Wang
2. Aminu Da’u
3. Xingchao Peng
4. Jindong Wang
5. Huili Huang
6. Chaohui Yu
7. Jian Shen
8. Mohammad Rostami
9. Mohammadreza Iman
10. Werner Zellinger

Papers from Fuzhen Zhuang's perspective

Ordered by how strongly the researchers Fuzhen Zhuang 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A Comprehensive Survey on Transfer Learning (2019) — 5,955 citations
• CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding (2026)
• Deep Subdomain Adaptation Network for Image Classification (2020) — 1,091 citations
• A Survey on Knowledge Graph-Based Recommender Systems (2020) — 1,001 citations
• Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources (2019) — 381 citations
• Graph Contextualized Self-Attention Network for Session-based Recommendation (2019) — 703 citations
• Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019) — 478 citations
• Sequential Recommender System based on Hierarchical Attention Networks (2018) — 423 citations
• A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018) — 37 citations
• Attention-driven Factor Model for Explainable Personalized Recommendation (2018) — 57 citations
• Representation learning via Dual-Autoencoder for recommendation (2017) — 107 citations
• Supervised Representation Learning: Transfer Learning with Deep Autoencoders (2015) — 370 citations
• Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (2015) — 22 citations
• Embedding with Autoencoder Regularization (2013) — 48 citations
• Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective (2010) — 69 citations