Alternatives and similar repositories for Correlated-VAEs

Users that are interested in Correlated-VAEs are comparing it to the libraries listed below

• YingzhenLi / VRbound
  code release for the NIPS 2016 paper
  ☆27Updated 8 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated 7 months ago
• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 5 years ago
• cambridge-mlg / SPVAE
  Tensorflow code for "Hierarchical Decompositional Mixtures of Variational Autoencoders" (ICML'19)
  ☆12Updated 5 years ago
• paruby / Wasserstein-Auto-Encoders
  Contains code relating to this arxiv paper https://arxiv.org/abs/1802.03761
  ☆37Updated 7 years ago
• didriknielsen / ds3_abi_exercises
  Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018
  ☆22Updated 6 years ago
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆65Updated 4 years ago
• JACKHAHA363 / BBBRNN
  Bayesian Backprop RNN implementation pytorch https://arxiv.org/abs/1704.02798
  ☆25Updated 7 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 6 years ago
• MichaelArbel / Scaled-MMD-GAN
  Scaled MMD GAN
  ☆36Updated 5 years ago
• markvdw / GPflow-inter-domain
  Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables
  ☆13Updated 7 years ago
• lxuechen / inference-suboptimality
  Code for "Inference Suboptimality in Variational Autoencoders"
  ☆14Updated 5 years ago
• USC-Melady / KDD19-CoSTCo
  ☆22Updated 2 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 6 years ago
• IraKorshunova / bruno
  a deep recurrent model for exchangeable data
  ☆34Updated 4 years ago
• ucla-vision / information-dropout
  Implementation of Information Dropout
  ☆39Updated 7 years ago
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆43Updated 4 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 6 years ago
• ron-amit / meta-learning-adjusting-priors
  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
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago
• SamsungLabs / deep-weight-prior
  The Deep Weight Prior, ICLR 2019
  ☆45Updated 4 years ago
• blei-lab / ars-reparameterization
  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
  ☆39Updated 8 years ago
• da-molchanov / variance-networks
  Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
  ☆39Updated 5 years ago
• mducoffe / Learning-Wasserstein-Embeddings
  Keras implementation of Deep Wasserstein Embeddings
  ☆48Updated 7 years ago
• ashwindcruz / dgm
  Deep Generative Models (Chainer)
  ☆10Updated 7 years ago
• chriscremer / Inference-Suboptimality
  Code for 'Inference Suboptimality in Variational Autoencoders'
  ☆10Updated 5 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 5 years ago
• wittawatj / interpretable-test
  NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
  ☆63Updated 7 years ago