Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Bennani, Younes"'
Autor:
Ghannou, Omar, Bennani, Younès
Multi-source Domain Adaptation (MDA) seeks to adapt models trained on data from multiple labeled source domains to perform effectively on an unlabeled target domain data, assuming access to sources data. To address the challenges of model adaptation
Externí odkaz:
http://arxiv.org/abs/2404.06599
Autor:
Salem, Nosseiba Ben, Bennani, Younes, Karkazan, Joseph, Barbara, Abir, Dacheux, Charles, Gregory, Thomas
Deep learning-based applications have seen a lot of success in recent years. Text, audio, image, and video have all been explored with great success using deep learning approaches. The use of convolutional neural networks (CNN) in computer vision, in
Externí odkaz:
http://arxiv.org/abs/2307.08880
Domain adaptation arises as an important problem in statistical learning theory when the data-generating processes differ between training and test samples, respectively called source and target domains. Recent theoretical advances show that the succ
Externí odkaz:
http://arxiv.org/abs/2210.13331
Finding the failure scenarios of a system is a very complex problem in the field of Probabilistic Safety Assessment (PSA). In order to solve this problem we will use the Hidden Quantum Markov Models (HQMMs) to create a generative model. Therefore, in
Externí odkaz:
http://arxiv.org/abs/2204.00087
In this paper we provide the quantum version of the Convex Non-negative Matrix Factorization algorithm (Convex-NMF) by using the D-wave quantum annealer. More precisely, we use D-wave 2000Q to find the low rank approximation of a fixed real-valued ma
Externí odkaz:
http://arxiv.org/abs/2203.15634
Autor:
Orgiu, Antoni, Karkazan, Bihes, Cannell, Stuart, Dechaumet, Léo, Bennani, Younes, Grégory, Thomas
Publikováno v:
In Hand Surgery and Rehabilitation September 2024 43(4)
Publikováno v:
2021 International Conference on Neural Information Processing (ICONIP)
In this paper, we tackle the inductive semi-supervised learning problem that aims to obtain label predictions for out-of-sample data. The proposed approach, called Optimal Transport Induction (OTI), extends efficiently an optimal transport based tran
Externí odkaz:
http://arxiv.org/abs/2112.07262
In this paper, we propose a novel approach for unsupervised domain adaptation, that relates notions of optimal transport, learning probability measures and unsupervised learning. The proposed approach, HOT-DA, is based on a hierarchical formulation o
Externí odkaz:
http://arxiv.org/abs/2112.02073
Publikováno v:
2021 International Joint Conference on Neural Networks (IJCNN)
In this paper, we tackle the transductive semi-supervised learning problem that aims to obtain label predictions for the given unlabeled data points according to Vapnik's principle. Our proposed approach is based on optimal transport, a mathematical
Externí odkaz:
http://arxiv.org/abs/2110.01446
This paper deals with a clustering algorithm for histogram data based on a Self-Organizing Map (SOM) learning. It combines a dimension reduction by SOM and the clustering of the data in a reduced space. Related to the kind of data, a suitable dissimi
Externí odkaz:
http://arxiv.org/abs/2109.04301