Search Results for author:
Found 7 papers, 2 papers with code
StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation
Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains.
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations
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.
Functional Autoencoder for Smoothing and Representation Learning
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.
Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps
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.
Neural Networks for Scalar Input and Functional Output
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.
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data.
FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces
Neural networks have excelled at regression and classification problems when the input space consists of scalar variables.
