Zobrazeno 1 - 10
of 394
pro vyhledávání: '"Glatard Tristan"'
The optimal training of a vision transformer for brain encoding depends on three factors: model size, data size, and computational resources. This study investigates these three pillars, focusing on the effects of data scaling, model scaling, and hig
Externí odkaz:
http://arxiv.org/abs/2410.19810
Publikováno v:
Concurrency and Computation: Practice and Experience (2023) 35(21):e7635
The general increase in data size and data sharing motivates the adoption of Big Data strategies in several scientific disciplines. However, while several options are available, no particular guidelines exist for selecting a Big Data engine. In this
Externí odkaz:
http://arxiv.org/abs/2406.01409
Magnetic Resonance Image (MRI) pre-processing is a critical step for neuroimaging analysis. However, the computational cost of MRI pre-processing pipelines is a major bottleneck for large cohort studies and some clinical applications. While High-Perf
Externí odkaz:
http://arxiv.org/abs/2405.17650
Neuroimaging open-data initiatives have led to increased availability of large scientific datasets. While these datasets are shifting the processing bottleneck from compute-intensive to data-intensive, current standardized analysis tools have yet to
Externí odkaz:
http://arxiv.org/abs/2404.11556
Brain encoding with neuroimaging data is an established analysis aimed at predicting human brain activity directly from complex stimuli features such as movie frames. Typically, these features are the latent space representation from an artificial ne
Externí odkaz:
http://arxiv.org/abs/2403.19421
Autor:
Germani, Elodie, Baghwat, Nikhil, Dugré, Mathieu, Gau, Rémi, Montillo, Albert, Nguyen, Kevin, Sokolowski, Andrzej, Sharp, Madeleine, Poline, Jean-Baptiste, Glatard, Tristan
Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have b
Externí odkaz:
http://arxiv.org/abs/2403.15405
Autor:
Bordeau-Aubert, Korantin, Whatley, Justin, Nadeau, Sylvain, Glatard, Tristan, Jaumard, Brigitte
The increasing complexity and scale of telecommunication networks have led to a growing interest in automated anomaly detection systems. However, the classification of anomalies detected on network Key Performance Indicators (KPI) has received less a
Externí odkaz:
http://arxiv.org/abs/2308.16279
Autor:
Pepe, Inés Gonzalez, Sivakolunthu, Vinuyan, Park, Hae Lang, Chatelain, Yohan, Glatard, Tristan
This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding -- a stochastic arithmetic technique -- to CNN models employed in non-linear registratio
Externí odkaz:
http://arxiv.org/abs/2308.01939
Autor:
Chatelain, Yohan, Tetrel, Loïc, Markiewicz, Christopher J., Goncalves, Mathias, Kiar, Gregory, Esteban, Oscar, Bellec, Pierre, Glatard, Tristan
Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitable software updates and hardware evolutions. This paper introduces a nu
Externí odkaz:
http://arxiv.org/abs/2307.01373
Publikováno v:
Vol 19, no. 1 (2024): e0296725
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well wi
Externí odkaz:
http://arxiv.org/abs/2212.06361