most citedRobust discovery of partial differential equations in complex situations

4 citations · 10 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI20221 cited

Planning Assembly Sequence with Graph Transformer

Lin Ma, Jiangtao Gong, Hao Xu +4

Assembly sequence planning (ASP) is the essential process for modern manufacturing, proven to be NP-complete thus its effective and efficient solution has been a challenge for rese…

cs.LG20212 cited

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning

Justin Li, Dakang Zhang, Yifei Wang +3

Stereoselective reactions (both chemical and enzymatic reactions) have been essential for origin of life, evolution, human biology and medicine. Since late 1960s, there have been n…

cs.LG20214 cited

Robust discovery of partial differential equations in complex situations

Hao Xu, Dongxiao Zhang

Data-driven discovery of partial differential equations (PDEs) has achieved considerable development in recent years. Several aspects of problems have been resolved by sparse regre…

cs.LG20213 cited

Topological Regularization for Graph Neural Networks Augmentation

Rui Song, Fausto Giunchiglia, Ke Zhao +1

The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision. In this paper, we propose a featur…

cs.CV2021

OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration

Hao Xu, Shuaicheng Liu, Guangfu Wang +2

Point cloud registration is a key task in many computational fields. Previous correspondence matching based methods require the inputs to have distinctive geometric structures to f…