Search Results for author:
Found 4 papers, 2 papers with code
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities.
Gradients of Connectivity as Graph Fourier Bases of Brain Activity
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience.
Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks
In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning.
End-to-end learning of energy-based representations for irregularly-sampled signals and images
In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, i. e. when the training data involved missing data.
