Bayesian neural networks increasingly sparsify their units with depth

effects for the best model (one-neuron, one-layer and linear activation function) on the simulated QTLMAS2010 data.. The dominance, over-dominance and under-dominance loci were

We developed a novel method (NetSparse) that uses Bayesian neural networks with variable selection for the prediction of genotypic values of individuals, includ- ing

We provide a large probability bound on the uniform approximation of fractional Brownian motion with Hurst parameter H , by a deep-feedforward ReLU neural network fed with a

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette.. To cite this version: Gaël

In this paper, we evaluate the efficiency of using CNNs for source camera model identification based on deep learning and convolutional neural networks.. The contribution repre- sents

Chaumont, ”Camera Model Identification Based Machine Learning Approach With High Order Statistics Features”, in EUSIPCO’2016, 24th European Signal Processing Conference 2016,

Elles peuvent être fréquentes en partie également dans la « Microquellregion » (région des très petites sources de la plage) mais sont cependant limitées en

The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.. L’archive ouverte pluridisciplinaire HAL, est