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)
Code for the ongoing project on improving inference in variational autoencoder by using real non volume preserving transformations(rNVP).
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
List of papers and code for relevant Generative Models
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).
Understanding normalizing flows
My solution to the NeurIPS challenge Learn to Move: Walk Around
Normalizing flows for density estimation with built-in support for sampling.
Reimplementations of some normalizing flow algorithms using tensorflow 2.1 and tensorflow probability 0.9
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
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