karlnapf / kernel_goodness_of_fitLinks
Code for the paper "A Kernel Test of Goodness of Fit" by Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
☆25Updated 9 years ago
Alternatives and similar repositories for kernel_goodness_of_fit
Users that are interested in kernel_goodness_of_fit are comparing it to the libraries listed below
Sorting:
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 6 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆64Updated 7 years ago
- Code for "Deep Convolutional Networks as shallow Gaussian Processes"☆39Updated 6 years ago
- Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)☆45Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Gaussian Processes in Pytorch☆75Updated 5 years ago
- Sparse-input neural networks☆23Updated 2 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Deep convolutional gaussian processes.☆80Updated 6 years ago
- Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018☆26Updated 7 years ago
- Graduate topics course on learning discrete latent structure.☆67Updated 6 years ago
- a deep recurrent model for exchangeable data☆34Updated 5 years ago
- ☆25Updated 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
- Practical sessions for the Optimal Transport and Machine learning course at DS3 2018☆90Updated 7 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆38Updated 7 years ago
- gpbo☆25Updated 4 years ago
- TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials☆31Updated 7 years ago
- Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses☆190Updated last year
- ☆11Updated 7 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆106Updated 7 years ago
- Lua implementation of Entropy-SGD☆82Updated 7 years ago
- TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network☆183Updated 7 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 5 years ago
- Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)☆42Updated 5 years ago
- ☆12Updated 2 years ago
- Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)☆38Updated 8 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 8 years ago
- Material for the practical of the DS3 course on "Representing and comparing probabilities with kernels"☆26Updated 6 years ago
- Tensorflow implementation of preconditioned stochastic gradient descent☆34Updated last year