Search Results for author:
Found 6 papers, 3 papers with code
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets.
Parametrization of stochastic inputs using generative adversarial networks with application in geology
The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations.
Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks
We synthesize new realizations such that the discrepancy in the patch distribution between the realizations and the exemplar image is minimized.
Parametric generation of conditional geological realizations using generative neural networks
This inference network is a neural network trained to sample a posterior distribution derived using a Bayesian formulation of the conditioning task.
A machine learning approach for efficient uncertainty quantification using multiscale methods
Several multiscale methods account for sub-grid scale features using coarse scale basis functions.
Parametrization and generation of geological models with generative adversarial networks
One of the main challenges in the parametrization of geological models is the ability to capture complex geological structures often observed in the subsurface.
