Xiuqiang He

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 9 papers; trusts 161 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it; #21,143 of 35,345 by trust here, #7,112 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #13,242 of 21,575 by trust here, #6,542 by citations
• Information Systems — 74 citations into it; #3,842 of 8,771 by trust here, #1,086 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Recommender Systems — 25 citations into it; #155 of 233 by trust here, #80 by citations
• Model Architectures — 17 citations into it
• Graph Learning — 14 citations into it
• Marketing — 12 citations into it

Whom Xiuqiang He trusts overall

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

1. Steffen Rendle
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Kaiming He
8. Ilya Sutskever
9. Oriol Vinyals
10. Karen Simonyan

Who trusts Xiuqiang He overall

1. Minghong Fang
2. Huifeng Guo
3. Chuhan Wu
4. Guolei Yang
5. Ruiming Tang

Papers from Xiuqiang He's perspective

Ordered by how strongly the researchers Xiuqiang He 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017) — 3,129 citations
• Federated Meta-Learning with Fast Convergence and Efficient Communication (2018) — 475 citations
• The Supplementary Material: Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding (2020)
• AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction (2020) — 206 citations
• Mining target users for online marketing based on App Store data (2015) — 10 citations
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (2021) — 471 citations
• AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction (2020) — 60 citations
• Federated Meta-Learning for Recommendation (2018) — 140 citations
• A Graph-Based Push Service Platform (2016) — 6 citations