blt2114 / CDE_with_BNF
Toy Examples of Conditional Density Estimation with Bayesian Normalizing flows
☆22Updated 6 years ago
Alternatives and similar repositories for CDE_with_BNF:
Users that are interested in CDE_with_BNF are comparing it to the libraries listed below
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Sequential Neural Likelihood☆40Updated 5 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 5 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated 10 months ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆42Updated 8 months ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago
- Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021☆24Updated 2 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆83Updated 4 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 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
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆37Updated 3 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆72Updated 2 years ago
- Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…☆45Updated last year
- ☆28Updated 6 years ago
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆48Updated 4 years ago
- Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances☆47Updated last year
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- Codes for ICLR 21 paper: Neural Approximate Sufficient Statistics for Implicit Models☆19Updated 2 years ago
- A PyTorch re-implementation of "Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives"☆18Updated 5 years ago
- Code for the ICML 2019 paper: Distribution Calibration for Regression☆21Updated last year
- ☆40Updated 5 years ago
- ☆53Updated 9 months ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago