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Gengchen Mai

University of Texas at Austin

32 papers hereh-index 313.4k citations82 works total

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

author position
  • first author14
  • middle author15
  • last author1

Across the 30 of 32 papers where every author was matched, so the position is known.

fields
  • cs.AI11
  • cs.CV7
  • cs.CL5
  • cs.LG4
  • cs.DB2
  • cs.CY1
affiliations
  • University of Texas at Austin
  • University of Georgia
  • Google
Homepage
same name
  • Gengchen Mai — 18 papers, h 10
  • Gengchen Mai — 7 papers, h 13
  • Gengchen Mai — 4 papers, h 3
  • Gengchen Mai — 1 paper
  • Gengchen Mai — 1 paper, h 2
  • Gengchen Mai — 1 paper, h 3

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
20182026
most citedA Review of Location Encoding for GeoAI: Methods and Applications

170 citations · 634 across the 30 of their papers we have counts for

collaborators
Showing 2020Show all

3 papers · 1 filter

cs.DB2020★ 87 cited

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

Gengchen Mai, Krzysztof Janowicz, Ling Cai +5

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answ…

cs.IR2020★ 1 cited

Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

Gengchen Mai, Krzysztof Janowicz, Sathya Prasad +5

Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to pr…

cs.CV2020★ 15 cited

Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

Gengchen Mai, Krzysztof Janowicz, Bo Yan +3

Unsupervised text encoding models have recently fueled substantial progress in NLP. The key idea is to use neural networks to convert words in texts to vector space representations…

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