mkocaoglu / CausalGANLinks
☆144Updated 7 years ago
Alternatives and similar repositories for CausalGAN
Users that are interested in CausalGAN are comparing it to the libraries listed below
Sorting:
- Learning kernels to maximize the power of MMD tests☆210Updated 7 years ago
- Scaled MMD GAN☆36Updated 5 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆64Updated 7 years ago
- Deep Generative Models with Stick-Breaking Priors☆96Updated 9 years ago
- ☆43Updated 6 years ago
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆50Updated 4 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 6 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Causal Inference & Deep Learning, MIT IAP 2018☆89Updated 7 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- ☆91Updated 6 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 9 years ago
- Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"☆100Updated 6 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- ☆32Updated 7 years ago
- Implementation of the Sliced Wasserstein Autoencoders☆91Updated 7 years ago
- Code for "Neural causal learning from unknown interventions"☆104Updated 5 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆53Updated 4 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆131Updated 4 years ago
- Code for "Towards a learning theory of cause-effect inference" (ICML 2015).☆30Updated 4 years ago
- Code for Invariant Rep. Without Adversaries (NIPS 2018)☆35Updated 5 years ago
- python code for kernel methods☆40Updated 6 years ago
- Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"☆101Updated 9 years ago
- This repository contains the code to reproduce the core results from the paper "Adversarial Variational Bayes: Unifying Variational Autoe…☆206Updated 7 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- MisGAN: Learning from Incomplete Data with GANs☆82Updated last year
- PyTorch implementation of Neural Processes☆89Updated 6 years ago
- Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE☆40Updated 2 years ago