Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Fromont, Élisa"'
The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. However, generating tabular data is not straightforward due to its hete
Externí odkaz:
http://arxiv.org/abs/2406.12945
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines used to compute fMRI statistic maps can be considered as
Externí odkaz:
http://arxiv.org/abs/2404.03703
Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different
Externí odkaz:
http://arxiv.org/abs/2312.14493
Publikováno v:
IEEE International Conference on Image Processing, Oct 2024, Abu Dhabi, United Arab Emirates. \&\#x27E8;10.1109/ICIP51287.2024.10647701\&\#x27E9
Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline. While it has been shown that the use of different pipelines might lead to different results, there is still
Externí odkaz:
http://arxiv.org/abs/2312.06231
lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LCE), a machine learning method that further enhances the prediction per
Externí odkaz:
http://arxiv.org/abs/2308.07250
Autor:
Voyez, Antonin, Allard, Tristan, Avoine, Gildas, Cauchois, Pierre, Fromont, Elisa, Simonin, Matthieu
The collection of electrical consumption time series through smart meters grows with ambitious nationwide smart grid programs. This data is both highly sensitive and highly valuable: strong laws about personal data protect it while laws about open da
Externí odkaz:
http://arxiv.org/abs/2211.07205
Context. We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a se
Externí odkaz:
http://arxiv.org/abs/2209.10099
Publikováno v:
Complex Feedback in Online Learning Workshop at the 39th International Conference on Machine Learning, Jul 2022, Baltimore, United States
We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expe
Externí odkaz:
http://arxiv.org/abs/2208.01515
We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful sets of sequential rules. The rules are selected using an MDL-inspired c
Externí odkaz:
http://arxiv.org/abs/2109.07519
Most deep learning object detectors are based on the anchor mechanism and resort to the Intersection over Union (IoU) between predefined anchor boxes and ground truth boxes to evaluate the matching quality between anchors and objects. In this paper,
Externí odkaz:
http://arxiv.org/abs/2009.14085