Basarab Mateï
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Applications of Machine Learning Technology in Agricultural Data Mining Open
The global agricultural sector is undergoing a revolutionary transformation with the growing integration of machine learning (ML) technologies into traditional farming and agronomic practices [...]
CADMR: Cross-Attention and Disentangled Learning for Multimodal Recommender Systems Open
The increasing availability and diversity of multimodal data in recommender systems offer new avenues for enhancing recommendation accuracy and user satisfaction. However, these systems must contend with high-dimensional, sparse user-item …
Multimodal Machine Learning for Sign Language Prediction Open
Numerous applications, including translation tools, interpreting services, video remote interpreting, human-computer interaction, online hand tracking of human communication in desktop settings, real-time multi-person recognition systems, …
Unsupervised Knowledge Extraction from Biomedical Data Open
In this paper we introduce a study on the use of the unsupervised representation learning on biomedical data i.e. on Growth weight data and Wisconsin Diagnostic Breast Cancer obtaining good performances in terms of clustering In this study…
Joint Multi-View Collaborative Clustering Open
International audience
Rethinking Collaborative Clustering: A Practical and Theoretical Study Within the Realm of Multi-view Clustering Open
With distributed and multi-view data being more and more ubiquitous, the last 20 years have seen a surge in the development of new multi-view methods. In unsupervised learning, these are usually classified under the paradigm of multi-view …
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization Open
By combining related objects, unsupervised machine learning techniques aim to\nreveal the underlying patterns in a data set. Non-negative Matrix Factorization\n(NMF) is a data mining technique that splits data matrices by imposing\nrestric…
D6.8: State of the art of SAT and PSA solvers in the light of quantum computing Open
The main objective of this report is to understand the main factors that may help to solve fault tree analysis problems using quantum algorithms. It turns out that fault tree analysis can be considered an extended variant of Boolean Satisf…
A quantum learning approach based on Hidden Markov Models for failure scenarios generation Open
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. Th…
Convex Non-negative Matrix Factorization Through Quantum Annealing Open
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 rea…
Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering Open
The aim of collaborative clustering is to enhance the performances of clustering algorithms by enabling them to work together and exchange their information to tackle difficult data sets. The fundamental concept of collaboration is that cl…
Impact of Learners’ Quality and Diversity in Collaborative Clustering Open
Collaborative Clustering is a data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. The aim of collaborative clustering is to reveal the common underlying structure of data…
HDR Image Tone Mapping Histogram Adjustment with Using An Optimized Contrast Parameter Open
International audience
Existence of quasicrystals and universal stable sampling and interpolation in LCA groups Open
We characterize all the locally compact abelian (LCA) groups that contain quasicrystals (a class of model sets). Moreover, we describe all possible quasicrystals in the group constructing an appropriate lattice associated with the cut and …
HDR Image Tone Mapping Approach based on Near Optimal Separable Adaptive Lifting Scheme Open
International audience
An Information Theory based Approach to Multisource Clustering Open
Clustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algo…
Online Semi-supervised Growing Neural Gas for Multi-label Data Classification Open
International audience
Learning Useful Representations Through Stacked Self-Organizing Maps Open
International audience
Revisited histogram equalization as HDR images tone mapping operators Open
International audience
Image tone mapping approach using essentially non-oscillatory bi-quadratic interpolations combined with a weighting coefficients strategy Open
International audience
Co-clustering through Optimal Transport Open
In this paper, we present a novel method for co-clustering, an unsupervised learning approach that aims at discovering homogeneous groups of data instances and features by grouping them simultaneously. The proposed method uses the entropy …
Signal-based autonomous clustering for relational data Open
International audience
Improved sparse prototyping for relational K-means Open
International audience
Existence of quasicrystals and universal stable sampling and interpolation in LCA groups Open
We characterize all the locally compact abelian (LCA) groups that contain quasicrystals (a class of model sets). Moreover, we describe all possible quasicrystals in the group constructing an appropriate lattice associated with the cut and …