alshedivat / keras-gp
Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
☆249Updated 5 months ago
Alternatives and similar repositories for keras-gp:
Users that are interested in keras-gp are comparing it to the libraries listed below
- We use a modified neural network instead of Gaussian process for Bayesian optimization.☆108Updated 7 years ago
- Deep Gaussian Processes in Python☆232Updated 3 years ago
- Bayesian Optimization using GPflow☆269Updated 4 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 8 years ago
- Convolutional Gaussian processes based on GPflow.☆96Updated 7 years ago
- Bayesian optimization for Python☆242Updated 2 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆148Updated 5 years ago
- Additional kernels that can be used with scikit-learn's Gaussian Process module☆80Updated 6 months ago
- Implementing Bayes by Backprop☆183Updated 5 years ago
- Structured Inference Networks for Nonlinear State Space Models☆267Updated 7 years ago
- A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation☆127Updated 3 years ago
- A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.☆266Updated 4 years ago
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆248Updated 6 years ago
- Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"☆376Updated 8 years ago
- Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al☆217Updated 6 years ago
- Neural Processes implementation for 1D regression☆65Updated 5 years ago
- What My Deep Model Doesn't Know...☆117Updated 9 years ago
- Gaussian Processes in Pytorch☆75Updated 4 years ago
- Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood☆94Updated 7 years ago
- Kalman Variational Auto-Encoder☆135Updated 5 years ago
- pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.☆214Updated 5 years ago
- Dropout As A Bayesian Approximation: Code☆201Updated 9 years ago
- Deep neural network kernel for Gaussian process☆203Updated 4 years ago
- ☆231Updated 7 years ago
- Tools for loading standard data sets in machine learning☆203Updated 2 years ago
- Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326☆71Updated 6 years ago
- Variational Fourier Features☆83Updated 3 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☆181Updated 10 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆105Updated 6 years ago
- Collection of jupyter notebooks for demonstrating software.☆165Updated last year