I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

Gaussian Processes for Experimental Sciences

Deep and Machine Learning for Microscopy

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

Code that accompanies the paper Guided Deep Kernel Learning

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."

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

Evaluating Deep Gaussian processes