dilinwang820 / Stein-Variational-Gradient-DescentLinks
code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
☆409Updated last year
Alternatives and similar repositories for Stein-Variational-Gradient-Descent
Users that are interested in Stein-Variational-Gradient-Descent are comparing it to the libraries listed below
Sorting:
- Deep neural network kernel for Gaussian process☆211Updated 5 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆151Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆106Updated 7 years ago
- I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth☆186Updated 11 years ago
- Deep Gaussian Processes in Python☆236Updated 4 years ago
- Input Convex Neural Networks☆298Updated 6 years ago
- ☆239Updated 8 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆248Updated last year
- The collection of papers about combining deep learning and Bayesian nonparametrics☆121Updated 5 years ago
- code for Structured Variational Autoencoders☆351Updated 7 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Kalman Variational Auto-Encoder☆136Updated 6 years ago
- Structured Inference Networks for Nonlinear State Space Models☆273Updated 8 years ago
- Black Box Variational Inference☆14Updated 10 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆100Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)☆355Updated 8 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- Masked Autoregressive Flow☆218Updated last year
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated 11 months ago
- Implementation of Stochastic Gradient MCMC algorithms☆41Updated 8 years ago
- Neural Processes implementation for 1D regression☆64Updated 6 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)☆236Updated 7 years ago
- Deep Gaussian Processes in matlab☆93Updated 4 years ago
- Code for "A-NICE-MC: Adversarial Training for MCMC"☆126Updated 7 years ago
- Understanding normalizing flows☆132Updated 5 years ago