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Xing Xie

16 papers hereh-index 172k citations27 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author10
  • last author6

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.IR5
  • cs.AI2
  • cs.CL2
  • cs.CV1
same name
  • Xing Xie — 35 papers, h 94
  • Xing Xie — 30 papers, h 24
  • Xing Xie — 21 papers, h 18
  • Xing Xie — 21 papers, h 7
  • Xing Xie — 21 papers, h 15
  • Xing Xie — 18 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedGraphGAN: Graph Representation Learning with Generative Adversarial Nets

179 citations · 535 across the 16 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023

Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models

Peiyan Zhang, Haoyang Liu, Chaozhuo Li +3

Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in…

cs.AI2023★ 3 cited

ConvFormer: Revisiting Transformer for Sequential User Modeling

Hao Wang, Jianxun Lian, Mingqi Wu +5

Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behav…

cs.CL2023

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion

Rui Li, Xu Chen, Chaozhuo Li +8

Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize int…

cs.LG2023★ 45 cited

Continual Learning on Dynamic Graphs via Parameter Isolation

Peiyan Zhang, Yuchen Yan, Chaozhuo Li +4

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetti…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.