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researcher

Li Guo

4 papers hereh-index 418k citations213 works total

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

author position
  • middle author1
  • last author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI2
  • cs.IR1
  • cs.LG1
same name
  • Li Guo — 105 papers, h 35
  • Li Guo — 18 papers, h 9
  • Li Guo — 15 papers, h 6
  • Li Guo — 14 papers, h 17
  • Li Guo — 12 papers, h 2
  • Li Guo — 9 papers

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
20172019
most citedDeep Active Learning for Anchor User Prediction

30 citations · 68 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2019★ 30 cited

Deep Active Learning for Anchor User Prediction

Anfeng Cheng, Chuan Zhou, Hong Yang +4

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consi…

cs.AI2018

Improving Knowledge Graph Embedding Using Simple Constraints

Boyang Ding, Quan Wang, Bin Wang +1

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent a…

cs.IR2018★ 15 cited

Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval

Jing Yu, Yuhang Lu, Zengchang Qin +4

Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a…

cs.AI2017★ 23 cited

Knowledge Graph Embedding with Iterative Guidance from Soft Rules

Shu Guo, Quan Wang, Lihong Wang +2

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing…

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