Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
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Updated
Apr 9, 2024 - Python
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Awesome resources on normalizing flows.
Normalizing flows in PyTorch
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
An extension of XGBoost to probabilistic modelling
DGMs for NLP. A roadmap.
PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.
Repository for Deep Structural Causal Models for Tractable Counterfactual Inference
Normalizing flows in PyTorch
An extension of LightGBM to probabilistic modelling
Manifold-learning flows (ℳ-flows)
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"
Network-to-Network Translation with Conditional Invertible Neural Networks
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
Code for reproducing Flow ++ experiments
Pytorch implementation of Block Neural Autoregressive Flow
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
A Julia framework for invertible neural networks
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