4 citations · 11 across the 13 of their papers we have counts for
14 papers
On the modality gap and the contrastive loss in multi-modal representation learning
Fabian Mager, Hiba Nassar, Lars Kai Hansen
We study the modality gap in CLIP-style dual-encoder contrastive learning, where image and text embeddings remain misaligned despite being trained in a shared space. We argue that…
Improving Relative Representations with Learned Anchors and Whitened Inner Products
Oscar Thorsted Svendsen, Nikolaj Holst Jakobsen, Fabian Mager +1
Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI systems. While Relative Represent…
On What We Can Learn from Low-Resolution Data
Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar +1
Artificial intelligence systems typically rely on large, centrally collected datasets, a premise that does not hold in many real-world domains such as healthcare and public institu…
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…
Splinets -- Orthogonal Splines and FDA for the Classification Problem
Rani Basna, Hiba Nassar, Krzysztof Podgórski
This study introduces an efficient workflow for functional data analysis in classification problems, utilizing advanced orthogonal spline bases. The methodology is based on the fle…
Spline Based Methods for Functional Data on Multivariate Domains
Rani Basna, Hiba Nassar, Krzysztof Podgórski
Functional data analysis is typically performed in two steps: first, functionally representing discrete observations, and then applying functional methods to the so-represented dat…
Splinets 1.5.0 -- Periodic Splinets
Hiba Nassar, Krzysztof Podgórski
Periodic splines are a special kind of splines that are defined over a set of knots over a circle and are adequate for solving interpolation problems related to closed curves. This…
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…
A consistent estimator of the smoothing operator in the functional Hodrick-Prescott filter
Hiba Nassar
In this paper we consider a version of the functional Hodrick-Prescott filter for functional time series. We show that the associated optimal smoothing operator preserves the 'nois…
On the functional Hodrick-Prescott Filter with non-compact operators
Boualem Djehiche, Astrid Hilbert, Hiba Nassar
We study a version of the functional Hodrick-Prescott filter where the associated operator is not necessarily compact, but merely closed and densely defined with closed range. We s…
A Functional Hodrick Prescott Filter
Boualem Djehiche, Hiba Nassar
We propose a functional version of the Hodrick-Prescott filter for functional data which take values in an infinite dimensional separable Hilbert space. We further characterize the…