rflamary / OTML_DS3_2018
Practical sessions for the Optimal Transport and Machine learning course at DS3 2018
☆87Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for OTML_DS3_2018
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆54Updated 5 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆110Updated 5 years ago
- Python implementation of smooth optimal transport.☆56Updated 3 years ago
- Keras implementation of Deep Wasserstein Embeddings☆46Updated 6 years ago
- Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018☆26Updated 6 years ago
- Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge☆60Updated last year
- Graduate topics course on learning discrete latent structure.☆66Updated 5 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆55Updated 6 years ago
- Understanding normalizing flows☆131Updated 4 years ago
- ☆89Updated 5 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆80Updated 4 years ago
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆50Updated 7 years ago
- Gaussian Process Prior Variational Autoencoder☆79Updated 5 years ago
- ☆67Updated 5 years ago
- Scaled MMD GAN☆36Updated 5 years ago
- Code for Sliced Gromov-Wasserstein☆66Updated 4 years ago
- Optimal transport transforms☆20Updated 6 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆118Updated 5 years ago
- The codebase for the paper "A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks"☆21Updated 5 years ago
- ☆159Updated 3 months ago
- The collection of recent papers about variational inference☆84Updated 5 years ago
- ☆121Updated last year
- Implementation of the Sliced Wasserstein Autoencoders☆91Updated 6 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 4 years ago
- paper lists and information on mean-field theory of deep learning☆75Updated 5 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- The Deep Weight Prior, ICLR 2019☆44Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 4 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆81Updated 5 months ago