gpytorch
There are 27 repositories under gpytorch topic.
BayesWatch/deep-kernel-transfer
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments
mbiparva/ax-bo-image-classification
Hyperpatameter Bayesian Optimization for Image Classification in PyTorch
silvery107/bayesian-opt-gpytorch
Bayesian Optimization for MPPI Control of Robot Arm Planar Pushing
Unco3892/UncertaintyPlayground
A Fast and Simplified Python Library for Uncertainty Estimation
TheJacksonLab/ECG_ActiveLearning
Dataset and code for "Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning"
yucho147/GP
We have created a module to run the Gaussian process model. We have implemented the code based on GPyTorch.
Devin-Taylor/gpcnn
Uncertainty in convolutional neural network predictions using Gaussian processes
HariKrishnan06082k/Robot-Learning-for-Planning-and-Control
Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning.
smsharma/gamma-gp
Proof-of-principle application of Gaussian process modeling to gamma-ray analyses. Code repository associated with the paper https://arxiv.org/abs/2010.10450.
dmosopt/dmosopt
Distributed surrogate-assisted evolutionary methods for multi-objective optimization of high-dimensional dynamical systems
JBris/prefect-surrogate-models
Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.
stanbiryukov/apollo
Highly performant and scalable out-of-the-box gaussian process regression and Bernoulli classification. Built upon GPyTorch, with a familiar sklearn api.
0xyuqi/physics-informed-kriging-pro
Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE + 核深度学习 + 多保真 Co-Kriging + 主动采样的物理约束克里金方法，用于复杂时空环境建模与预测
AhmetZamanis/DeepLearningEnergyForecasting
Time series forecasting on an hourly energy dataset, with LSTM & Transformer models implemented in PyTorch Lightning. Deployment of the Transformer model using Docker, with GPU support.
hanyuanz2000/Sparse-Gaussian-Process-for-Missing-Heart-Rate-Data-Imputation
Explores the application of Gaussian Process (GP) and sparse GP algorithms to handle missing heart rate time series dataset. Our findings emphasize the importance of kernel selection, specifically the RBF kernel, and the careful tuning of hyperparameters to achieve optimal performance in imputation tasks
richardcsuwandi/gp
Implementation of Gaussian Process (GP) models using GPyTorch.
wearepal/ethicml-models
Models for EthicML
aswathyrk93/LAMP_CODE
Contains code for Adaptive protection platform in Smart grids
elcorto/gp_playground
Explore selected topics related to Gaussian processes
JBris/aws-ray-cluster
Testing deployment of a Ray cluster on AWS
JBris/deepgp_eval
Evaluating Deep Gaussian processes
JBris/zero-inflated-gaussian-processes
Latent gaussian processes for zero inflated count data.
supersjgk/Cyclist_Stress
Implementation of Cyclist Pressure Research Paper
joseph-nagel/gaussian-processes
Gaussian process explorations
nonconvexopt/pytorch-SSGE
Unofficial PyTorch Reimplementation of the Spectral Stein Gradient Estimator, compatiable to use gpytorch kernel modules.
Cef0PT/famgpytorch
An implementation of an approximated RBF kernel using GPyTorch