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)
Normalizing flows in PyTorch
Awesome resources on normalizing flows.
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"
An extension of XGBoost to probabilistic modelling
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.
PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Pytorch implementation of Block Neural Autoregressive Flow
Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)
Code for reproducing Flow ++ experiments
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
An extension of LightGBM to probabilistic modelling
Network-to-Network Translation with Conditional Invertible Neural Networks
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
Normalizing flows in PyTorch
Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.
Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)
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