Zobrazeno 1 - 10
of 33 935
pro vyhledávání: '"Yassine, A"'
In this paper, we explore the construction of Planar Maximally Entangled (PME) states from phase states. PME states form a class of $n$-partite states in which any subset of adjacent particles whose size is less than or equal to half the total number
Externí odkaz:
http://arxiv.org/abs/2411.15077
Autor:
Lachemat, Houssam Eddine-Othman, Abbas, Akli, Oukas, Nourredine, Kheir, Yassine El, Haboussi, Samia, Shammur, Absar Chowdhury
The paper introduces and publicly releases (Data download link available after acceptance) CAFE -- the first Code-switching dataset between Algerian dialect, French, and english languages. The CAFE speech data is unique for (a) its spontaneous speaki
Externí odkaz:
http://arxiv.org/abs/2411.13424
Autor:
Yahalomi, Daniel A., Kipping, David, Solano-Oropeza, Diana, Li, Madison, Poddar, Avishi, Xunhe, Zhang, Abaakil, Yassine, Cassese, Benjamin, Teachey, Alex, Liu, Jiajing, Sundai, Farai, Valaskovic, Lila
Accurate, precise, and computationally efficient removal of unwanted activity that exists as a combination of periodic, quasi-periodic, and non-periodic systematic trends in time-series photometric data is a critical step in exoplanet transit analysi
Externí odkaz:
http://arxiv.org/abs/2411.09753
Autor:
Abbahaddou, Yassine, Malliaros, Fragkiskos D., Lutzeyer, Johannes F., Aboussalah, Amine Mohamed, Vazirgiannis, Michalis
Graph Neural Networks (GNNs) have shown great promise in tasks like node and graph classification, but they often struggle to generalize, particularly to unseen or out-of-distribution (OOD) data. These challenges are exacerbated when training data is
Externí odkaz:
http://arxiv.org/abs/2411.08638
We investigate determinantal varieties for symmetric matrices that have zero blocks along the main diagonal. In theoretical physics, these arise as Gram matrices for kinematic variables in quantum field theories. We study the ideals of relations amon
Externí odkaz:
http://arxiv.org/abs/2411.08624
Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has advantages inclu
Externí odkaz:
http://arxiv.org/abs/2411.07348
Autor:
Abbahaddou, Yassine, Ennadir, Sofiane, Lutzeyer, Johannes F., Malliaros, Fragkiskos D., Vazirgiannis, Michalis
Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns about their real-world applicability. While existing defense technique
Externí odkaz:
http://arxiv.org/abs/2411.05399
The superior performance introduced by deep learning approaches in removing atmospheric particles such as snow and rain from a single image; favors their usage over classical ones. However, deep learning-based approaches still suffer from challenges
Externí odkaz:
http://arxiv.org/abs/2411.04821
Graph Shift Operators (GSOs), such as the adjacency and graph Laplacian matrices, play a fundamental role in graph theory and graph representation learning. Traditional GSOs are typically constructed by normalizing the adjacency matrix by the degree
Externí odkaz:
http://arxiv.org/abs/2411.04655
This paper presents an approach to semi-supervised learning for the classification of data using the Lipschitz Learning on graphs. We develop a graph-based semi-supervised learning framework that leverages the properties of the infinity Laplacian to
Externí odkaz:
http://arxiv.org/abs/2411.03273