87 citations · 205 across the 8 of their papers we have counts for
Showing cs.CVShow all
3 papers · 1 filter
cs.CV2022★ 3 cited
Towards General-Purpose Representation Learning of Polygonal Geometries
Gengchen Mai, Chiyu Jiang, Weiwei Sun +6
Neural network representation learning for spatial data is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made…
cs.CV2022★ 2 cited
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions
Gengchen Mai, Yao Xuan, Wenyun Zuo +2
Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such a…
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…