87 citations · 133 across the 11 of their papers we have counts for
11 papers
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…
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Data-driven discovery of PDEs has made tremendous progress recently, and many canonical PDEs have been discovered successfully for proof-of-concept. However, determining the most p…
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…
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Yuntian Chen, Yingtao Luo, Qiang Liu +2
Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes…
FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
Hao Xu, Nianjin Ye, Guanghui Liu +2
Data association is important in the point cloud registration. In this work, we propose to solve the partial-to-partial registration from a new perspective, by introducing multi-le…
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Junsheng Zeng, Hao Xu, Yuntian Chen +1
Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of li…
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…
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…
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…
Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations
Ze Cheng, Juncheng Li, Chenxu Wang +4
In the problem of learning disentangled representations, one of the promising methods is to factorize aggregated posterior by penalizing the total correlation of sampled latent var…
Hybrid Affinity Propagation
Jingdong Wang, Hao Xu, Xian-Sheng Hua +1
In this paper, we address a problem of managing tagged images with hybrid summarization. We formulate this problem as finding a few image exemplars to represent the image set seman…