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Found 9 papers, 0 papers with code
Latent Matters: Learning Deep State-Space Models
Our results show that the constrained optimisation framework significantly improves system identification and prediction accuracy on the example of established state-of-the-art DSSMs.
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
We represent the posterior approximation of the network weights by a diagonal Gaussian distribution and a complementary memory of raw data.
Learning Flat Latent Manifolds with VAEs
Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry.
FLAT MANIFOLD VAES
Latent-variable models represent observed data by mapping a prior distribution over some latent space to an observed space.
Increasing the Generalisation Capacity of Conditional VAEs
We address the problem of one-to-many mappings in supervised learning, where a single instance has many different solutions of possibly equal cost.
Learning Hierarchical Priors in VAEs
We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution.
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity.
Active Learning based on Data Uncertainty and Model Sensitivity
It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i. e., transitions that lead to abrupt changes in the movement of the robot.
Metrics for Deep Generative Models
Neural samplers such as variational autoencoders (VAEs) or generative adversarial networks (GANs) approximate distributions by transforming samples from a simple random source---the latent space---to samples from a more complex distribution represented by a dataset.
