Xin Xia

Artificial Intelligence · IEEE Transactions on Knowledge and Data Engineering · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 79 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #21,459 of 35,345 by trust here, #17,412 by citations
• Information Systems — 92 citations into it; #4,570 of 8,771 by trust here, #3,315 by citations
• Computer Vision and Pattern Recognition — 67 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Graph Learning — 14 citations into it

Whom Xin Xia trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Sepp Hochreiter
6. Kaiming He
7. Yann LeCun
8. Ilya Sutskever
9. Steffen Rendle
10. J. Urgen Schmidhuber

Who trusts Xin Xia overall

1. Junliang Yu
2. Hongzhi Yin
3. Tong Chen
4. Shiwen Wu
5. Chen Gao
6. Xubin Ren

Papers from Xin Xia's perspective

Ordered by how strongly the researchers Xin Xia 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Self-Supervised Learning for Recommender Systems: A Survey (2022) — 438 citations
• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022) — 401 citations
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021) — 990 citations
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020) — 632 citations