4 citations · 6 across the 4 of their papers we have counts for
5 papers
Efficient spline orthogonal basis for representation of density functions
Jana Burkotová, Ivana Pavlů, Hiba Nassar +2
Probability density functions form a specific class of functional data objects with intrinsic properties of scale invariance and relative scale characterized by the unit integral c…
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning
Mads Emil Dahlgaard, Morten Wehlast Jørgensen, Niels Asp Fuglsang +1
The idea of federated learning is to train deep neural network models collaboratively and share them with multiple participants without exposing their private training data to each…
Machine Learning Assisted Orthonormal Basis Selection for Functional Data Analysis
Rani Basna, Hiba Nassar, Krzysztof Podgórski
In implementations of the functional data methods, the effect of the initial choice of an orthonormal basis has not gained much attention in the past. Typically, several standard b…
Minimal Model Structure Analysis for Input Reconstruction in Federated Learning
Jia Qian, Hiba Nassar, Lars Kai Hansen
\ac{fl} proposed a distributed \ac{ml} framework where every distributed worker owns a complete copy of global model and their own data. The training is occurred locally, which ass…
Splinets -- efficient orthonormalization of the B-splines
Xijia Liu, Hiba Nassar, Krzysztof PodgÓrski
A new efficient orthogonalization of the B-spline basis is proposed and contrasted with some previous orthogonalized methods. The resulting orthogonal basis of splines is best visu…