DartML / Stein-Variational-Gradient-Descent
code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
☆99Updated 5 years ago
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
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆82Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆23Updated 3 years ago
- Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for mod…☆118Updated 3 months ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated 9 months ago
- Deep neural network kernel for Gaussian process☆203Updated 4 years ago
- The collection of recent papers about variational inference☆85Updated 5 years ago
- PyTorch implementation of Stein Variational Gradient Descent☆43Updated last year
- Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.☆58Updated 4 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆149Updated 6 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆53Updated 5 months ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆120Updated 5 years ago
- Random Fourier Features☆50Updated 7 years ago
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆48Updated 4 years ago
- Code for "Function Space Particle Optimization for Bayesian Neural Networks"☆17Updated 2 years ago
- Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)☆22Updated 5 years ago
- Testing methods for estimating KL-divergence from samples.☆62Updated last week
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Continual Gaussian Processes☆32Updated last year
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆97Updated 11 months ago
- Extensible Tensorflow library for differentiable particle filtering. ICML 2021.☆41Updated 2 years ago
- Masked Autoregressive Flow☆210Updated 7 months ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 3 years ago
- Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)☆33Updated last year
- Code for the paper Gaussian process behaviour in wide deep networks☆48Updated 6 years ago