Ai Gaussian Processes | AI Final Year Project Report

Complete Technical Paper Content — Ai Gaussian Processes

1. Executive Abstract, Problem Formulation & Background

d2l-ai / d2l-en Star 30k Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. python data-science machine-learning natural-language-processing reinforcement-learning computer-vision deep-learning mxnet book notebook tensorflow keras pytorch kaggle hyperparameter-optimization recommender-system gaussian-processes jax Updated Aug 18, 2024 Python

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

ddbourgin / numpy-ml Star 16.3k Machine learning, in numpy machine-learning reinforcement-learning word2vec lstm neural-networks gaussian-mixture-models vae topic-modeling attention resnet bayesian-inference wavenet mfcc knn gaussian-processes hidden-markov-models gradient-boosting wgan-gp good-turing-smoothing Updated Oct 29, 2023 Python

bayesian-optimization / BayesianOptimization Star 8.7k A Python implementation of global optimization with gaussian processes. python simple optimization gaussian-processes bayesian-optimization Updated Oct 16, 2026 Python

cornellius-gp / gpytorch Star 3.9k A highly efficient implementation of Gaussian Processes in PyTorch pytorch gpu-acceleration gaussian-processes Updated Oct 2, 2026 Python

google / neural-tangents Star 2.4k Fast and Easy Infinite Neural Networks in Python kernel neural-networks gradient-descent bayesian-inference gaussian-processes bayesian-networks deep-networks gradient-flow jax infinite-networks training-dynamics neural-tangents kernel-computation Updated Mar 1, 2024 Jupyter Notebook

GPflow / GPflow Star 1.9k Gaussian processes in TensorFlow machine-learning deep-learning tensorflow ml gaussian-processes variational-inference bayesian-statistics markov-chain-monte-carlo stochastic-processes gp gpflow Updated Oct 15, 2026 Python

krasserm / bayesian-machine-learning Star 1.9k Notebooks about Bayesian methods for machine learning machine-learning bayesian-methods gaussian-processes bayesian-optimization bayesian-machine-learning variational-autoencoder Updated Jul 12, 2026 Jupyter Notebook

2. Related Work, Literature Survey & Algorithmic Foundations

GeoStat-Framework / PyKrige Star 864 Kriging Toolkit for Python interpolation spatial-analysis gaussian-processes geostatistics kriging spatial-statistics Updated Oct 16, 2025 Python

fabsig / GPBoost Star 704 Tree-Boosting, Gaussian Processes, and Mixed-Effects Models python data-science machine-learning r cpp artificial-intelligence mixed-effects gaussian-processes boosting Updated Oct 30, 2026 C++

QuantClimate / GPJax Star 670 Gaussian processes in JAX and Equinox. machine-learning probabilistic-programming bayesian-inference gaussian-processes jax Updated Oct 27, 2026 Python

jrvansuita / GaussianBlur Star 508 🎩 An easy and fast library to apply gaussian blur filter on any images. android image canvas filter bitmap image-processing blur gaussian drawable filters renderscript image-compression gaussian-processes gaussian-filter blur-backgrounds blurry gaussianblur Updated Apr 28, 2022 Java

dfm / george Star 461 Fast and flexible Gaussian Process regression in Python python c-plus-plus time-series cpp gaussian-processes Updated Jun 22, 2026 C++

ARM-software / mango Star 418 Parallel Hyperparameter Tuning in Python machine-learning neural-network parallel-computing neural-networks hyperparameter-optimization tuning-parameters gaussian-processes bayesian-optimization hyperparameter-tuning cluster-deployment sklearn-compatible kubernetes-deployment tensorflow-examples blackbox-optimization production-system keras-examples scipy-compatible pytorch-compatible Updated Mar 11, 2026 Jupyter Notebook

ohirose / bcpd Sponsor Star 376 Domain Elastic Transform / Bayesian Coherent Point Drift outlier-detection bayesian-inference gaussian-processes variational-inference shape-analysis point-cloud-registration shape-matching coherent-point-drift non-rigid-registration downsampling point-set-registration shape-deformation function-registration Updated Oct 24, 2026 C

gtrll / gpmp2 Star 358 Gaussian Process Motion Planner 2 robotics inference motion-planning trajectory-optimization gaussian-processes Updated Aug 28, 2022 C++

3. Dataset Ingestion, Preprocessing & Stochastic Augmentations

JuliaGaussianProcesses / Stheno.jl Star 343 Probabilistic Programming with Gaussian processes in Julia machine-learning probabilistic-programming bayesian-inference gaussian-processes approximate-inference Updated Apr 1, 2025 Julia

nrontsis / PILCO Star 336 Bayesian Reinforcement Learning in Tensorflow machine-learning reinforcement-learning tensorflow gaussian-processes model-based-rl Updated Feb 15, 2021 Python

thuijskens / bayesian-optimization Star 316 Python code for bayesian optimization using Gaussian processes optimization hyperparameter-optimization gaussian-processes Updated Dec 6, 2016 Jupyter Notebook

JuliaGaussianProcesses / AbstractGPs.jl Star 289 Abstract types and methods for Gaussian Processes. julia gps probabilistic-programming gaussian-processes Updated Oct 18, 2026 Julia

hoangcuong2011 / Good-Papers Star 286 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 machine-learning natural-language-processing computer-vision deep-learning neural-network semi-supervised-learning convolutional-networks neural-machine-translation representation-learning gaussian-processes variational-inference kernel-learning variational-autoencoders deep-kernel-learning Updated Jun 5, 2019

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

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Deep GP The Python Implementation of Deep Gaussian Processes Currently implemented models are Deep GPs Variational Auto-encoded Deep GPs Testing GitHub Actions runs the unit tests on pull requests and pushes to main . To run the suite locally: python -m pip install " numpy>=1.7 " " scipy>=0.12 " " GPy>=1.0 " matplotlib h5py tables python -m pip install -e . --no-deps python -m unittest deepgp.testing.model_tests_basic -v Or with tox : tox

4. Neural Network Backbone, Layer Specifications & Loss Formulations

python -m pip install " numpy>=1.7 " " scipy>=0.12 " " GPy>=1.0 " matplotlib h5py tables python -m pip install -e . --no-deps python -m unittest deepgp.testing.model_tests_basic -v

Resources on Gaussian Processes Gaussian processes (GPs) are a challenging area of Bayesian machine learning to get started with – from wrapping your head around dealing with infinite dimensional Gaussian distributions, to understanding kernel functions and how to choose the right one for the right task, all on top of having solid knowledge of Bayesian inference. While primarily used as a powerful regression model with the ability to estimate uncertainty in predictions, GPs can also be used for classification, and have a very wide range of applications. These are some of the resources I have used, or are planning to use in my on-going process of learning about GPs. Lectures Iain Murray & Arno Onken (University of Edinburgh) "Machine Learning and Pattern Recognition" – requires a University of Edinburgh login This is a general course on machine learning, but focusing heavily on Bayesian aspects in the second half of the course, including GPs. This was my first introduction to GPs and Bayesian statistics, and both lecturers are great! Accompanying notes: Gaussian Processes , Gaussian Processes and Kernels Richard Turner (Universtiy of Cambridge) "Machine Learning Tutorial Series: Gaussian Processes" This is a great introduction, providing a unique way to visualize GPs that really helps with building intuition. Mauricio Alvarez (University of Sheffield) "Multi Output Gaussian Processes" An interesting look into the uses and challenges of multi-output GPs, and why to avoid independence assumptions. Videos Mutual Information – Duane Rich (YouTube) "Gaussian Processes" Another great introductory resource. This video offers a detailed yet higher level overview of GPs. Books Kevin P. Murphy "Machine Learning: A Probabilistic Perspective" , chapters 14 ( Kernels ) and 15 ( Gaussian processes ) Carl Edward Rasmussen & Christopher K. I. Williams "Gaussian Processes for Machine Learning" Note : There are some new books by Kevin Murphy that also have some GP content in them. Articles Marc Peter Deisenroth, Mark van der Wilk, Yicheng Luo "A Practical Guide to Gaussian Processes" A detailed GP guide with interactive tools for visualizing the effect of kernel hyper-parameters. Jochen Görtler, Rebecca Kehlbeck, Oliver Deussen "A Visual Exploration of Gaussian Processes" Another interactive guide, though goes into less detail about the mathematical theory of GPs than the above article. Yuge Shi "Gaussian Process, not quite for dummies" This blog post is essentially a written summary of the above linked GP tutorial lecture by Richard Turner. Martin Krasser "Gaussian processes" One of the few posts I could find that do a great job translating the theory of GPs into code examples without relying on existing GP frameworks, and instead building up from fundamental linear algebra using just NumPy . David Duvenaud "The Kernel Cookbook: Advice on Covariance functions" An extremely useful resource for deciding on kernel functions, and understanding how to combine them to model different combinations of effects including linear, exponential and periodic behaviour. Papers Seth Flaxman, Andrew Gelman, Daniel Neill, Alex Smola, Aki Vehtari, Andrew Gordon Wilson "Fast hierarchical Gaussian processes" While the main focus of this paper is on improving the computational efficiency of MCMC, the first two sections of the paper also provide a nice brief introduction on hierarchical Gaussian processes, where a hyper-prior is placed on the hyper-parameters of the kernel function. Applications These are just a few example applications involving GPs – there are many more areas where they are used! Carl Edward Rasmussen & Christopher K. I. Williams "Model Selection and Adaptation of Hyperparameters" in Gaussian Processes for Machine Learning Bayesian optimization is an increasingly popular approach for hyper-parameter tuning, as it explores the hyper-parameter search space in a more sophisticated and efficient way than grid/random searches. Shrihari Vasudevan, Fabio Ramos, Eric Nettleton, Hugh Durrant-Whyte, Allan Blair "Gaussian Process Modeling of Large Scale Terrain" This paper looks into how GPs can be used to estimate elevation information in unknown or inaccessible areas, based on recorded data from surrounding locations. The authors focus on field robotics applications such as mine exploration. Niya Chen, Zheng Qian, Xiaofeng Meng, Ian T. Nabney "Short-Term Wind Power Forecasting Using Gaussian Processes" Accurate forecasting for wind power can be difficult due to how erratic wind behaves. The authors use GPs to make one-day forecasts of wind power, with the aim of improving controlling strategies for wind turbines. Laura Martínez-Ferrer, María Piles, Gustau Camps-Valls "Crop Yield Estimation and Interpretability with Gaussian Processes" This paper looks at how satellite sensor observations and weather data can be combined with GPs to estimate the development and yield of crops. Elizabeth Buckingham-Jeffery, Valery Isham, Thomas House "Gaussian process approximations for fast inference from infectious disease data" With the recent global focus on epidemiology, it is interesting to see how GPs can be applied for deriving MLE population estimates for the number of infectious individuals with a disease at a given time. Packages Below is a list of some well-maintained Python packages that are commonly used to implement GPs. GPflow : A purpose built library just for GPs with a TensorFlow Probability back-end, focusing on making GPs more applicable to large-scale datasets. GPyTorch : Similar to GPflow, but PyTorch-based. PyMC3 and Pyro : General probabilistic machine learning packages which also have some GP functionality. There are other packages such as GPy which are quite feature rich, but seem to be far less maintained. Other Gaussian Process Summer Schools : Workshops for researchers interested in GP theory and application, normally held in Sheffield, UK. There are also a bunch of great GP resources on this website. Gaussian Processes Cambridge : An interest group for developing deeper understanding of GPs, also holding regular meetings (although it appears inactive since January 2021).

Gaussian processes (GPs) are a challenging area of Bayesian machine learning to get started with – from wrapping your head around dealing with infinite dimensional Gaussian distributions, to understanding kernel functions and how to choose the right one for the right task, all on top of having solid knowledge of Bayesian inference.

While primarily used as a powerful regression model with the ability to estimate uncertainty in predictions, GPs can also be used for classification, and have a very wide range of applications.

These are some of the resources I have used, or are planning to use in my on-going process of learning about GPs.

Note : There are some new books by Kevin Murphy that also have some GP content in them.

These are just a few example applications involving GPs – there are many more areas where they are used!

Below is a list of some well-maintained Python packages that are commonly used to implement GPs.

5. Training Protocol, Convergence Behavior & Experimental Evaluation

There are other packages such as GPy which are quite feature rich, but seem to be far less maintained.

Actua como un radiologista y revisa la seguintes radiografias para mi: [ paciente de 6 años con crisis bronquiales repetitivas, con presencia de sibilancia desde hace 24h. Optimizalo para hacerlo lo mejor y haz las preguntas que consideres necesarias antes de proceder]

Act as a radiologist and review the following x-rays for me: [6-year-old patient with repetitive bronchial attacks, wheezing for 24 hours. Optimize it to do the best and ask any questions you feel necessary before proceeding]

Deep convolutional gaussian processes This repository implements deep convolutional gaussian processes, a deep gaussian process model for hierarchically detecting combinations of local features in images. We've written about the method in our paper titled Deep Convolutional Gaussian Processes . We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve CIFAR-10 accuracy by over 10 percentage points. -- Kenneth Blomqvist, Samuel Kaski, Markus Heinonen The figures in the paper have been generated using this notebook . Setup This package uses the doubly stochastic deep gaussian process package. It has been included as a submodule to this repository. To install it run sh ./init.sh . This will initialize submodule and install the doubly stochastic deep gp package. To install other dependencies run pip install -r requirements.txt . Running experiments To run the mnist experiment run python conv_gp/mnist.py . Parameters and a number of options can be set using command line arguments. To see a full list of options run python conv_gp/mnist.py --help . The CIFAR-10 experiment located at conv_gp/cifar.py works similarly.

This repository implements deep convolutional gaussian processes, a deep gaussian process model for hierarchically detecting combinations of local features in images.

We've written about the method in our paper titled Deep Convolutional Gaussian Processes .