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Code for reproducing Flow ++ experiments

Pytorch implementation of Block Neural Autoregressive Flow

Invertiblenetworks.jl ⭐ 131

A Julia framework for invertible neural networks

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

Galevo23 Tutorials ⭐ 16

Tutorials for the KITP Galevo23 program

Probaforms ⭐ 16

Conditional normalizing flows (NFs), conditional GANs, and conditional variational autoencoders (CVAEs) with sklearn-like interface

Pytorch Nice ⭐ 12

Implementation of non-linear independent components estimation (NICE) in pytorch

Normalizing Flows ⭐ 6

This repository offers an implementation of some common architectures for normalizing flows.

Flowconductor ⭐ 5

(Conditional) Normalizing Flows in PyTorch. Offers a wide range of (conditional) invertible neural networks.

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