Reverse Dependencies of gpytorch

The following projects have a declared dependency on gpytorch:

• albert-toolkit — Python toolkit for Albert Invent
• alfi — An approximate latent force model library with variational inference for non-linear ODEs and PDEs.
• atomai — Deep and machine learning for atom-resolved data
• baybe — A Bayesian Back End for Design of Experiments
• beanmachine — Probabilistic Programming Language for Bayesian Inference
• beebo — Batched Energy-Entropy acquistion for Bayesian optimization
• blopt — Beamline optimization with machine learning
• bloptools — Beamline optimization with machine learning
• bocoel — Bayesian Optimization as a Coverage Tool for Evaluating Large Language Models
• bolift — BayesOPT with LIFT
• botorch — Bayesian Optimization in PyTorch
• catasta — Catasta is a Python library designed to simplify and accelerate the process of machine learning model experimentation. It encapsulates the complexities of model training and evaluation, offering researchers and developers a straightforward pipeline for rapid model assessment with minimal setup required.
• copulagp — A Copula-GP (Gaussian Process) package
• csle-common — Common functionality of the Cyber Security Learning Environment (CSLE)
• csle-system-identification — Scripts for system identification in CSLE
• discontinuum — Estimate discontinuous timeseries from continuous covariates.
• dklfm — no summary
• eagpytorch — We have created a module to run the Gaussian process model. We have implemented the code based on GPyTorch.
• edbojz — Bayesian reaction optimization as a tool for chemical synthesis.
• EMAI — Electron microscopy AI tools
• forecastblurdenoise — no summary
• gdrf — Pytorch+GPytorch implementation of GDRFs from San Soucie et al. 2020.
• GP-Plus-functions — A short description of your package
• GPErks — A Python library to (bene)fit Gaussian Process Emulators.
• gpforecaster — Hierarchical time series forecasting model using Gaussian Processes
• gpim — Gaussian processes for image analysis
• gpplus — Python Library for Generalized Gaussian Process Modeling
• gpyconform — Extends GPyTorch with Gaussian Process Regression Conformal Prediction
• gpytorch-lattice-kernel — Lattice kernel for scalable Gaussian processes in GPyTorch
• gpytorch-mogp — A package which extends GPyTorch with correlated multi-output GPs
• greattunes — Toolset for easy execution of Bayesian optimization for either step-by-step or closed-loop needs.
• harlow — Adaptive surrogate modelling
• HEBO — Heteroscedastic evolutionary bayesian optimisation
• hpo4dl — Hyper parameter optimization for deep learning
• htsmodels — Forecasting algorithms for hierarchical time series
• invariantkernels — Transformation-invariant kernels in GPyTorch
• ipp-toolkit — A general framework for informative path planning experiments, with a focus on wrapping datasets, sensors, planners, and visualization in a modular manner
• itabpfn — Interface for using interfeature TabPFN and library to train TabPFN'
• itabpfn-l1 — Interface for using interfeature TabPFN and library to train TabPFN'
• itabpfn2 — Interface for using interfeature TabPFN and library to train TabPFN'
• kats — kats: kit to analyze time series
• lightning-uq-box — Lighning-UQ-Box: A toolbox for uncertainty quantification in deep learning
• lotus-nlte — Determine atmospheric stellar parameters in non-LTE
• malt.wangyq.net — no summary
• moebius — Python package for optimizing peptide sequences using Bayesian optimization (BO)
• molflux — A foundational package for molecular predictive modelling
• multinterp — Multivariate Interpolation.
• nemo-bo — Multi-objective optimization of chemical processes with automated machine learning workflows
• netcal — The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
• nextorch — Experimental design and Bayesian optimization library in Python/PyTorch
• nlp-uncertainty-zoo — PyTorch Implementation of Models used for Uncertainty Estimation in Natural Language Processing.
• nubopy — A transparent Python package for Bayesian optimisation
• nussl — A flexible sound source separation library.
• obsidian_apo — Automated experiment design and black-box optimization
• OpenMEASURE — Python package for soft sensing applications
• optuna-integration — Integration libraries of Optuna.
• paref — Pareto reflection based multi-objective optimization
• petboa — Parameter Estimation using Bayesian Optimization
• pfns — PFNs made ready for BO
• pfns4bo — PFNs made ready for BO
• pgmuvi — A python package to interpret multiwavelength astronomical timeseries with GPs
• profit — Probabilistic response model fitting with interactive tools
• projectedlmc — A short package based on gpytorch implementing the Projected LMC model
• proteusAI — ProteusAI is a python package designed for AI driven protein engineering.
• pynm — ('Python implementation of Normative Modelling', 'with GAMLSS, Gaussian Processes, LOESS & Centiles approaches.')
• pytorch-dragon — A pytorch integrated Machine Learning / Deep learning utilities library
• pytorch-gpx — An official implementation of "Gaussian Process Regression With Interpretable Sample-Wise Feature Weights"
• quant1x — Quant1X量化交易框架
• QuickTune — Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
• rapid-models — Python package (Reciprocal Data and Physics models - RaPiD-models) to support more specific, accurate and timely decision support in operation of safety-critical systems, by combining physics-based modelling with data-driven machine learning and probabilistic uncertainty assessment.
• rctorch — A Python 3 toolset for creating and optimizing Echo State Networks. This library is an extension and expansion of the previous library written by Reinier Maat: https://github.com/1Reinier/Reservoir
• self-driving-lab-demo — Software and instructions for setting up and running an autonomous (self-driving) laboratory optics demo using dimmable RGB LEDs, a 10-channel spectrometer, a microcontroller, and an adaptive design algorithm.
• skorch — scikit-learn compatible neural network library for pytorch
• sober-bo — Fast Bayesian optimization, quadrature, inference over arbitrary domain (discrete and mixed spaces) with GPU parallel acceleration based on GPytorch and BoTorch.
• SPACEL — SPACEL: characterizing spatial transcriptome architectures by deep-learning
• spateo-release — Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics
• spatial-eggplant — Landmark-based transfer of spatial transcriptomics data
• svise — State estimation of a physical system with unknown governing equations
• swa-gaussian — SWA-Gaussian repo
• tabpfn — Interface for using TabPFN and library to train TabPFN'
• TabPFNbaseline — Interface for using interfeature TabPFN and library to train TabPFN'
• taxus — Gaussian Process models for transcriptome data
• tigramite — Tigramite causal inference for time series
• tsroots — Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
• uber-turbo — Packaged fork from uber-research/TuRBO
• UncertaintyPlayground — A Python library for uncertainty estimation in supervised learning tasks
• uq360 — Uncertainty Quantification 360
• waggon — WAsserstein Global Gradient-free OptimisatioN (WAGGON) methods library.
• wdbo-algo — W-DBO Algotithm for Dynamic Bayesian Optimization
• wenda-gpu — Fast domain adaptation for genomic data
• word2ket — word2ket is an effiecient embedding layer for PyTorch that is inspired by Quantum Entanglement.
• zellij — A software framework for HyperParameters Optimization