cornellius-gp/gpytorch
Gpytorch
GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware.
The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification.
The system employs numerical linear algebra techniques, including preconditioned conjugate gradients and tensors-based operations, to handle large datasets. It also supports black-box variational inference and automatic differentiation for hyperparameter optimization.
Features
- Gaussian Processes - Implements scalable Gaussian process models for probabilistic regression and uncertainty estimation using PyTorch.
- Hybrid Model Integration - Provides tools to combine Gaussian processes with deep learning frameworks to create hybrid predictive models.
- GPU-Accelerated Inference - Accelerates model predictions by executing complex linear algebra and matrix operations on graphics hardware.
- GPU-Accelerated Probabilistic Frameworks - Provides a GPU-accelerated framework for efficient matrix operations and conjugate gradient inference in probabilistic modeling.
- Kernel Composition - Allows the construction of complex covariance structures by combining simple kernels through addition and multiplication.
- Probabilistic Machine Learning - Provides scalable probabilistic learning capabilities for large datasets through efficient numerical techniques.
- Probabilistic Hybrid Models - A system for combining Gaussian processes with deep neural networks to build hybrid models for regression and classification.
- Covariance Kernels - Defines the relationship between data points using modular functions that compute the covariance matrix for Gaussian processes.
- Automatic Differentiation - Utilizes automatic differentiation to compute gradients of the marginal likelihood for hyperparameter optimization.
- Kernel Composition Frameworks - Provides a modular framework for constructing custom covariance functions and kernels to model data dependencies.
- Variational Inference - Implements black-box variational inference to approximate posterior distributions and scale to large datasets.
- High-Performance Linear Algebra - Executes high-dimensional tensor-based matrix operations on GPUs to accelerate posterior distribution computations.
- Preconditioned Iterative Solvers - Employs preconditioned conjugate gradients to solve large linear systems without performing full matrix inversion.
- Deep Learning Ecosystems - Gaussian process library for PyTorch.
- Deep Learning Frameworks - Gaussian process library for PyTorch.
- Machine Learning Frameworks - Gaussian process library built on PyTorch.
- Probabilistic Modeling - Efficient Gaussian process implementation for PyTorch.
- Data and Graph Processing - Modular Gaussian process implementation.
- Developer Tools - Efficient Gaussian process implementation.
Star history
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