We present an automated data augmentation approach for image classification. formulate the problem as Monte Carlo sampling where our goal is to approximate optimal policies. propose a particle filtering formulation find policies and their schedules during model training. Our performance measurement procedure relies on validation subset of training set, while policy transition depends Gaussian p...

This article presents a new method to sample on manifolds, based the Dirichlet distribution. The proposed strategy allows completely respect underlying manifold around which data are observed, and do massive sampling with low computational effort. can be very helpful, for instance, in neural networks training process, as well uncertainty analysis stochastic optimization. Due its simplicity effi...

Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species–habitat relationships abundance across space time. Distance sampling (DS) capture-recapture (CR) two widely collected data types abundance; still, they seldomly SDMs due lack of spatial coverage. However, fusion sources can increase coverage, which reduce paramet...