Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)
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Updated
Jan 26, 2017 - Python
Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
OpenAI Glow implementation for TPU/GPU
Continuous-time gradient flow for generative modeling and variational inference
Implementation of Normalizing flows on MNIST https://arxiv.org/abs/1505.05770
Code for reproducing Flow ++ experiments
Density Estimation and Anomaly Detection with Normalizing Flows
Some tricks to improve normalizing flows
Deep learning techniques on classification tasks (MLP, CNN), analysis of sequential data (RNN) and implementation of generative models (VAE, GAN and NF).
My solution to the NeurIPS challenge Learn to Move: Walk Around
Code for the paper "Semi-Conditional Normalizing Flows for Semi-Supervised Learning"
Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" https://arxiv.org/abs/1904.02399
Implementation of non-linear independent components estimation (NICE) in pytorch
A pytorch implementation of the most commonly used normalising flows.
An implementation of variational normalizing flows using TF2
meta-RL soft actor-critic with BRUNO for task inference
Library for Normalizing Flow in TensorFlow 2.0.
Flow-based generative model for 3D point clouds.
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