marionmari / pyGPsLinks
pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
☆217Updated 6 years ago
Alternatives and similar repositories for pyGPs
Users that are interested in pyGPs are comparing it to the libraries listed below
Sorting:
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆251Updated last year
- Additional kernels that can be used with scikit-learn's Gaussian Process module☆82Updated last year
- Kernel structure discovery research code - likely to be unstable☆193Updated 10 years ago
- Collection of jupyter notebooks for demonstrating software.☆169Updated 2 years ago
- Bayesian Optimization using GPflow☆272Updated 5 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☆188Updated 11 years ago
- ☆241Updated 8 years ago
- Deep Gaussian Processes in matlab☆93Updated 4 years ago
- Bayesian optimization for Python☆246Updated 3 years ago
- Structured Inference Networks for Nonlinear State Space Models☆275Updated 8 years ago
- A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.☆272Updated 5 years ago
- Bayesian nonparametric machine learning for Python☆233Updated 2 years ago
- Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.☆191Updated 6 years ago
- Python package for modular Bayesian optimization☆137Updated 4 years ago
- Optimizers for machine learning☆183Updated 2 years ago
- BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.☆421Updated 2 years ago
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 8 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆110Updated 7 years ago
- A tutorial about Gaussian process regression☆191Updated 5 years ago
- Deep Gaussian Processes in Python☆236Updated 4 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 8 years ago
- Bayesian optimization in high-dimensions via random embedding.☆116Updated 12 years ago
- Parameterization Framework for parameterized model creation and handling.☆49Updated 5 months ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆151Updated 6 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago
- Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"☆380Updated 8 years ago
- megaman: Manifold Learning for Millions of Points☆332Updated 2 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆410Updated last year
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 11 years ago
- RoBO: a Robust Bayesian Optimization framework☆490Updated 6 years ago