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Found 7 papers, 1 papers with code
Deep Learning the Shape of the Brain Connectome
To statistically study the variability and differences between normal and abnormal brain connectomes, a mathematical model of the neural connections is required.
Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold.
Structural Connectome Atlas Construction in the Space of Riemannian Metrics
The structural connectome is often represented by fiber bundles generated from various types of tractography.
The Adversarial Attack and Detection under the Fisher Information Metric
By considering the data space as a non-linear space with the Fisher information metric induced from a neural network, we first propose an adversarial attack algorithm termed one-step spectral attack (OSSA).
The Riemannian Geometry of Deep Generative Models
Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space.
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently.
Local Group Invariant Representations via Orbit Embeddings
We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations.
