rflamary / OTML_DS3_2018Links
Practical sessions for the Optimal Transport and Machine learning course at DS3 2018
☆90Updated 6 years ago
Alternatives and similar repositories for OTML_DS3_2018
Users that are interested in OTML_DS3_2018 are comparing it to the libraries listed below
Sorting:
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆55Updated 6 years ago
- Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018☆26Updated 7 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆84Updated 4 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Code for some of the experiments I did with variational autoencoders on multi-modality and atari video prediction. Atari video prediction…☆62Updated 8 years ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- Understanding normalizing flows☆132Updated 5 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆72Updated 8 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆113Updated 6 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆57Updated 6 years ago
- Deep convolutional gaussian processes.☆78Updated 5 years ago
- Python implementation of smooth optimal transport.☆58Updated 4 years ago
- Testing Nerual Tangent Kernel (NTK) on small UCI datasets☆81Updated 5 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆121Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code for Sliced Gromov-Wasserstein☆68Updated 5 years ago
- L. Chizat, G. Peyré, B. Schmitzer, F-X. Vialard. Scaling Algorithms for Unbalanced Transport Problems. Preprint Arxiv:1607.05816, 2016.☆42Updated 8 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Optimal transport transforms☆22Updated 6 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- The collection of recent papers about variational inference☆85Updated 5 years ago
- Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)☆19Updated 6 years ago
- Learning generative models with Sinkhorn Loss☆28Updated 6 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 6 years ago
- Scaled MMD GAN☆36Updated 5 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 5 years ago
- ☆28Updated 3 years ago
- It is a repo which allows to compute all divergences derived from the theory of entropically regularized, unbalanced optimal transport. I…☆28Updated 2 years ago
- Experiments for the paper "Exponential expressivity in deep neural networks through transient chaos"☆71Updated 8 years ago