Normalising Flows
22 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Normalising Flow-based Differentiable Particle Filters
Recently, there has been a surge of interest in incorporating neural networks into particle filters, e. g. differentiable particle filters, to perform joint sequential state estimation and model learning for non-linear non-Gaussian state-space models in complex environments.
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
Deep generative models complement Markov-chain-Monte-Carlo methods for efficiently sampling from high-dimensional distributions.
MixerFlow for Image Modelling
Normalising flows are statistical models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model.
Testable Likelihoods for Beyond-the-Standard Model Fits
Studying potential BSM effects at the precision frontier requires accurate transfer of information from low-energy measurements to high-energy BSM models.
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation
Furthermore, our method excels in accurately retrieving complete 3D poses even in the presence of occlusions, making it highly applicable in situations where complete 2D pose information is unavailable.
Bayesian Exploration Networks
Empirical results demonstrate that BEN can learn true Bayes-optimal policies in tasks where existing model-free approaches fail.
Kernelised Normalising Flows
Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation.
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
The generation of virtual populations (VPs) of anatomy is essential for conducting in silico trials of medical devices.
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research.
Expressive, Variable, and Controllable Duration Modelling in TTS
First, we propose a duration model conditioned on phrasing that improves the predicted durations and provides better modelling of pauses.
