ys1998 / vae-latent-structureLinks
PyTorch implementation of "Variational Autoencoders with Jointly Optimized Latent Dependency Structure" [ICLR 2019]
☆13Updated 6 years ago
Alternatives and similar repositories for vae-latent-structure
Users that are interested in vae-latent-structure are comparing it to the libraries listed below
Sorting:
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated 11 months ago
- Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"☆127Updated 6 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 5 years ago
- Code to reproduce experiments in "Meta-learning probabilistic inference for prediction"☆69Updated 4 years ago
- A PyTorch implementation of the blocks from the _A Simple Neural Attentive Meta-Learner_ paper☆98Updated 7 years ago
- ☆78Updated 4 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Updated 5 years ago
- PyTorch implementation of Neural Processes☆89Updated 6 years ago
- An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU…☆72Updated 7 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆22Updated 5 years ago
- ☆13Updated 7 years ago
- A tensorflow implementation of VAE training with Renyi divergence☆31Updated 9 years ago
- code release for the NIPS 2016 paper☆28Updated 8 years ago
- Understanding Short-Horizon Bias in Stochastic Meta-Optimization☆37Updated 7 years ago
- Implementation of iterative inference in deep latent variable models☆43Updated 6 years ago
- Variational Autoencoders with Gaussian Mixture Latent Space☆36Updated 8 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Deep Generative Models with Stick-Breaking Priors☆96Updated 9 years ago
- Code for the "Neural Expectation Maximization" paper.☆126Updated 2 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Computing various norms/measures on over-parametrized neural networks☆50Updated 6 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- ☆43Updated 7 years ago
- ZForcing Repo☆40Updated 7 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"☆103Updated 9 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆33Updated 2 years ago
- A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"☆21Updated 6 years ago
- This repository contains implementations of the paper, Bayesian Model-Agnostic Meta-Learning.☆60Updated 6 years ago