no code implementations • 29 Mar 2024 • Sidi Wu, Yizi Chen, Samuel Mermet, Lorenz Hurni, Konrad Schindler, Nicolas Gonthier, Loic Landrieu
Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains.
no code implementations • 13 Mar 2024 • Hang Hu, Sidi Wu, Guoxiong Cai, Na Liu
In this work, a physics-driven GraphSAGE approach (PD-GraphSAGE) based on the Galerkin method and piecewise polynomial nodal basis functions is presented to solve computational problems governed by irregular PDEs and to develop parametric PDE surrogate models.
no code implementations • 17 Jan 2024 • Sidi Wu, Cédric Beaulac, Jiguo Cao
A common pipeline in functional data analysis is to first convert the discretely observed data to smooth functions, and then represent the functions by a finite-dimensional vector of coefficients summarizing the information.
1 code implementation • 19 Oct 2023 • Sidi Wu, Yizi Chen, Konrad Schindler, Lorenz Hurni
Even though our application is on segmenting historical maps, we believe that the method can be transferred into other fields with similar problems like temporal sequences of satellite images.
1 code implementation • 10 Aug 2022 • Sidi Wu, Cédric Beaulac, Jiguo Cao
In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs.
no code implementations • 8 Jul 2022 • Cédric Beaulac, Sidi Wu, Erin Gibson, Michelle F. Miranda, Jiguo Cao, Leno Rocha, Mirza Faisal Beg, Farouk S. Nathoo
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data.
no code implementations • 18 Sep 2020 • Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao
Neural networks have excelled at regression and classification problems when the input space consists of scalar variables.