Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Artieres, Thierry"'
One of the major challenges arising from single-cell transcriptomics experiments is the question of how to annotate the associated single-cell transcriptomic profiles. Because of the large size and the high dimensionality of the data, automated metho
Externí odkaz:
http://arxiv.org/abs/2409.05937
Autor:
Sicre, Ronan, Zhang, Hanwei, Dejasmin, Julien, Daaloul, Chiheb, Ayache, Stéphane, Artières, Thierry
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module. This system learns an
Externí odkaz:
http://arxiv.org/abs/2404.15037
Publikováno v:
Proceedings of the 39th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 2022
We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear and certain type of non-linear neural networks promotes low-rank solutio
Externí odkaz:
http://arxiv.org/abs/2207.08942
Publikováno v:
International Joint Conference on Neural Networks (IJCNN), Jul 2021, Online, China
Attempts of studying implicit regularization associated to gradient descent (GD) have identified matrix completion as a suitable test-bed. Late findings suggest that this phenomenon cannot be phrased as a minimization-norm problem, implying that a pa
Externí odkaz:
http://arxiv.org/abs/2105.01346
Autor:
Senoussi, Malek1 (AUTHOR), Artieres, Thierry1,2 (AUTHOR) thierry.artieres@univ-amu.fr, Villoutreix, Paul1,3 (AUTHOR) thierry.artieres@univ-amu.fr
Publikováno v:
PLoS Computational Biology. 4/5/2024, Vol. 20 Issue 4, p1-28. 28p.
Autor:
Sellami, Akrem, Dupé, François-Xavier, Cagna, Bastien, Kadri, Hachem, Ayache, Stéphane, Artières, Thierry, Takerkart, Sylvain
Publikováno v:
IJCNN 2020 - International Joint Conference on Neural Networks, Jul 2020, Glasgow, United Kingdom
In neuroscience, understanding inter-individual differences has recently emerged as a major challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable. For this, neuroscientists rely on basic methods such as univariate li
Externí odkaz:
http://arxiv.org/abs/2004.02804
Publikováno v:
IJCNN 2019 - International Joint Conference on Neural Networks, Jul 2019, Budapest, Hungary
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches. Here, we introduce a new architecture of neural networks in which we replace the top dense layers of standard convolutional architectures
Externí odkaz:
http://arxiv.org/abs/1911.13036
Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided at pixel level, building such
Externí odkaz:
http://arxiv.org/abs/1905.13539
The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underly
Externí odkaz:
http://arxiv.org/abs/1711.00305
We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches provide strategies to choose the examples to label before or during traini
Externí odkaz:
http://arxiv.org/abs/1706.08334