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researcher

Liang Zhao

University of Chinese Academy of Sciences

8 papers hereh-index 7212 citations22 works total

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

author position
  • middle author4
  • last author4

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AR1
  • cs.IR1
  • cs.MS1
  • math.FA1
affiliations
  • University of Chinese Academy of Sciences
  • Academy of Mathematics and Systems Science
ORCID 0000-0002-7543-6322
same name
  • Liang Zhao — 39 papers, h 21
  • Liang Zhao — 38 papers, h 75
  • Liang Zhao — 22 papers, h 13
  • Liang Zhao — 20 papers, h 27
  • Liang Zhao — 17 papers, h 7
  • Liang Zhao — 16 papers, h 23

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
20112023
most citedDeep Generative Model for Periodic Graphs

14 citations · 26 across the 8 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.IR2022★ 1 cited

Addressing the Extreme Cold-Start Problem in Group Recommendation

Guo linxin, Tao yinghui, Gao Min +3

The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems…

cs.AR2022★ 4 cited

COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search

Che-Kai Liu, Haobang Chen, Mohsen Imani +8

In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, p…

cs.LG2022★ 14 cited

Deep Generative Model for Periodic Graphs

Shiyu Wang, Xiaojie Guo, Liang Zhao

Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and polygon mesh. Their generative modeling has great potential in real-world application…

cs.LG2022

Efficiently Disentangle Causal Representations

Yuanpeng Li, Joel Hestness, Mohamed Elhoseiny +2

This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional probabilities in original and new d…

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