dtak / hs-bnn-publicLinks
☆12Updated 7 years ago
Alternatives and similar repositories for hs-bnn-public
Users that are interested in hs-bnn-public are comparing it to the libraries listed below
Sorting:
- Material for the practical of the DS3 course on "Representing and comparing probabilities with kernels"☆26Updated 6 years ago
- Implementation of linear CorEx and temporal CorEx.☆37Updated 4 years ago
- ☆12Updated 2 years ago
- Hierarchical Change-Point Detection☆14Updated 6 years ago
- NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.☆64Updated 7 years ago
- Variational Fourier Features☆85Updated 4 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 7 years ago
- python code for kernel methods☆40Updated 6 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- ☆40Updated 6 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)☆53Updated 4 years ago
- NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.☆67Updated 6 years ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 8 years ago
- Code for the icml paper "zero inflated exponential family embedding"☆29Updated 7 years ago
- a deep recurrent model for exchangeable data☆34Updated 5 years ago
- Stochastic Gradient Riemannian Langevin Dynamics☆34Updated 10 years ago
- Neural Processes implementation for 1D regression☆64Updated 6 years ago
- Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)☆38Updated 8 years ago
- Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…☆30Updated 5 years ago
- Dirichlet Process Mixture using PVI, SMC, Variational☆15Updated 11 years ago
- mean-field and structured VAEs for the IBP☆23Updated 7 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆39Updated 3 years ago
- ☆29Updated 6 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- State space modeling with recurrent neural networks☆45Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 7 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables☆13Updated 8 years ago