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Normalising Flows

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Showing 1–49 of 49 papers

TitleStatusHype
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows—0
Communicating Likelihoods with Normalising FlowsCode0
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
Marginal Causal Flows for Validation and InferenceCode0
emuflow: Normalising Flows for Joint Cosmological AnalysisCode0
Linear combinations of latents in generative models: subspaces and beyondCode0
Normalizing Flow-based Differentiable Particle FiltersCode1
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning—0
Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return DataCode0
MixerFlow: MLP-Mixer meets Normalising Flows—0
Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive FlowCode0
Testable Likelihoods for Beyond-the-Standard Model Fits—0
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation—0
Bayesian Exploration Networks—0
Kernelised Normalising Flows—0
NEnv: Neural Environment Maps for Global IlluminationCode1
Decorrelation using Optimal TransportCode0
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy—0
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows—0
OverFlow: Putting flows on top of neural transducers for better TTSCode1
Expressive, Variable, and Controllable Duration Modelling in TTS—0
Text-free non-parallel many-to-many voice conversion using normalising flows—0
Gradient estimators for normalising flows—0
Variational Gibbs Inference for Statistical Model Estimation from Incomplete DataCode0
Bootstrap Your FlowCode0
Sinusoidal Flow: A Fast Invertible Autoregressive FlowCode0
Implicit Riemannian Concave Potential Maps—0
Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows—0
Parallelised Diffeomorphic Sampling-based Motion Planning—0
Copula Flows for Synthetic Data Generation—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising FlowsCode0
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter ComplexityCode1
Latent Transformations for Discrete-Data Normalising Flows—0
Deep Structural Causal Models for Tractable Counterfactual InferenceCode1
Robust model training and generalisation with Studentising flowsCode1
Style-Controllable Speech-Driven Gesture Synthesis Using Normalising FlowsCode1
Woodbury Transformations for Deep Generative FlowsCode0
VFlow: More Expressive Generative Flows with Variational Data AugmentationCode1
Quinoa: a Q-function You Infer Normalized Over Actions—0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Relaxing Bijectivity Constraints with Continuously Indexed Normalising FlowsCode0
Localised Generative Flows—0
MoGlow: Probabilistic and controllable motion synthesis using normalising flowsCode1
Block Neural Autoregressive FlowCode0
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders—0
Conditional Density Estimation with Bayesian Normalising Flows—0
Implicit Weight Uncertainty in Neural NetworksCode0
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