Normalising Flows
22 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Text-free non-parallel many-to-many voice conversion using normalising flows
We investigate normalising flows for VC in both text-conditioned and text-free scenarios.
Gradient estimators for normalising flows
In this contribution we present new gradient estimator for Stochastic Gradient Descent algorithm (and the corresponding \texttt{PyTorch} implementation) and show that it leads to better training results for $\phi^4$ model.
Implicit Riemannian Concave Potential Maps
We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows.
Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows
One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
Parallelised Diffeomorphic Sampling-based Motion Planning
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP).
Copula Flows for Synthetic Data Generation
Learning the probabilistic model for the data is equivalent to estimating the density of the data.
Gaussian Process Latent Variable Flows for Massively Missing Data
The Bayesian incarnation of the GPLVM uses a variational framework, where the posterior over all unknown quantities is approximated by a well-behaved variational family, a factorised Gaussian.
Quinoa: a Q-function You Infer Normalized Over Actions
We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form.
Localised Generative Flows
We argue that flow-based density models based on continuous bijections are limited in their ability to learn target distributions with complicated topologies, and propose localised generative flows (LGFs) to address this problem.
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders
Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity.
