maka89 / Deep-Kernel-GPLinks
Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
☆93Updated 7 years ago
Alternatives and similar repositories for Deep-Kernel-GP
Users that are interested in Deep-Kernel-GP are comparing it to the libraries listed below
Sorting:
- Deep neural network kernel for Gaussian process☆206Updated 4 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- Multi-task Gaussian Process☆43Updated 10 years ago
- Deep Gaussian Processes in Python☆233Updated 4 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆37Updated 8 years ago
- Additive Gaussian Process Bandits - version 1.0☆27Updated 8 years ago
- ☆40Updated 6 years ago
- Deep convolutional gaussian processes.☆78Updated 5 years ago
- Convolutional Gaussian processes based on GPflow.☆96Updated 7 years ago
- Max-value Entropy Search for Efficient Bayesian Optimization☆74Updated 3 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 7 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated 10 months ago
- Code repository for Ensemble Bayesian Optimization☆53Updated 5 years ago
- Library for Deep Gaussian Processes based on GPflow☆19Updated 5 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Deep Gaussian Processes in matlab☆93Updated 3 years ago
- Bayesian neural network package☆147Updated 3 years ago
- Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables☆13Updated 7 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆84Updated 4 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 9 years ago
- ☆28Updated 6 years ago
- Random Fourier Features☆50Updated 8 years ago
- Asymmetric Transfer Learning with Deep Gaussian Processes☆18Updated 9 years ago