rflamary / OTML_DS3_2018Links
Practical sessions for the Optimal Transport and Machine learning course at DS3 2018
☆92Updated 7 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:
- Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018☆26Updated 7 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 6 years ago
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆57Updated 6 years ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Implementation of the Sliced Wasserstein Autoencoders☆91Updated 7 years ago
- Understanding normalizing flows☆132Updated 6 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆141Updated 7 years ago
- Graduate topics course on learning discrete latent structure.☆67Updated 6 years ago
- Deep convolutional gaussian processes.☆83Updated 6 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 6 years ago
- Scaled MMD GAN☆36Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆101Updated 7 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated last year
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆51Updated 4 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆93Updated 5 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆122Updated 6 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆58Updated 7 years ago
- Multislice PHATE for tensor embeddings☆61Updated 4 years ago
- ☆91Updated 6 years ago
- Hypergradient descent☆147Updated last year
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆64Updated 7 years ago
- Code for the paper "A Kernel Test of Goodness of Fit" by Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton☆25Updated 9 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆86Updated 5 years ago
- PyTorch implementation of Neural Processes☆88Updated 6 years ago
- Wasserstein / earth mover's distance visualizations☆66Updated 8 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆75Updated 9 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