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Normalising Flows
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Communicating Likelihoods with Normalising Flows
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
Marginal Causal Flows for Validation and Inference
$\mathtt{emuflow}$: Normalising Flows for Joint Cosmological Analysis
Linear combinations of latents in generative models: subspaces and beyond
Normalizing Flow-based Differentiable Particle Filters
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data
MixerFlow: MLP-Mixer meets Normalising Flows
Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow
Testable Likelihoods for Beyond-the-Standard Model Fits
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation
Bayesian Exploration Networks
Kernelised Normalising Flows
Decorrelation using Optimal Transport
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
OverFlow: Putting flows on top of neural transducers for better TTS
Expressive, Variable, and Controllable Duration Modelling in TTS
Text-free non-parallel many-to-many voice conversion using normalising flows
Gradient estimators for normalising flows
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Bootstrap Your Flow
Sinusoidal Flow: A Fast Invertible Autoregressive Flow
Implicit Riemannian Concave Potential Maps
Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows
Parallelised Diffeomorphic Sampling-based Motion Planning
Copula Flows for Synthetic Data Generation
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
Latent Transformations for Discrete-Data Normalising Flows
Deep Structural Causal Models for Tractable Counterfactual Inference
Robust model training and generalisation with Studentising flows
Woodbury Transformations for Deep Generative Flows
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.