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Found 5 papers, 0 papers with code
Geometry matters: Exploring language examples at the decision boundary
Using our approach, we explore difficult examples for several deep learning architectures.
Stochastic Image Deformation in Frequency Domain and Parameter Estimation using Moment Evolutions
We apply a stochastic generalisation of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework to model differences in the evolution of anatomical objects detected in populations of image data.
Deep Diffeomorphic Normalizing Flows
We use a neural network to parametrize the smooth vector field and a recursive neural network (RNN) for approximating the solution of the ODE.
Latent Space Non-Linear Statistics
Given data, deep generative models, such as variational autoencoders (VAE) and generative adversarial networks (GAN), train a lower dimensional latent representation of the data space.
Probabilistic Principal Geodesic Analysis
Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA) for dimensionality reduction of data on a Riemannian manifold.
