Gaussian Processes for Experimental Sciences
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Deep and Machine Learning for Microscopy
Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
Code that accompanies the paper Guided Deep Kernel Learning
[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Dataset and code for "Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning"
This repository contains code for paper: "Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images."
Add a description, image, and links to the deep-kernel-learning topic page so that developers can more easily learn about it.
To associate your repository with the deep-kernel-learning topic, visit your repo's landing page and select "manage topics."