phuijse / BLNNbookLinks
Bayesian Learning and Neural Networks (jupyter book sources)
☆56Updated 2 years ago
Alternatives and similar repositories for BLNNbook
Users that are interested in BLNNbook are comparing it to the libraries listed below
Sorting:
- Materials of the Nordic Probabilistic AI School 2022.☆179Updated 3 years ago
- Multi-Output Gaussian Process Toolkit☆175Updated 3 months ago
- ☆152Updated 3 years ago
- Public repo for course material on Bayesian machine learning at ENS Paris-Saclay and Univ Lille☆90Updated 6 months ago
- Introduction to Gaussian Processes☆29Updated 7 years ago
- Materials of the Nordic Probabilistic AI School 2023.☆90Updated last year
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆236Updated last year
- Recursive Bayesian Estimation (Sequential / Online Inference)☆59Updated last year
- legend☆209Updated 2 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆43Updated last year
- A curated list of resources for learning Gaussian Processes☆41Updated 4 years ago
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 2 years ago
- Notes for the Numerics of Machine Learning Lecture Course at the University of Tübingen☆215Updated last year
- Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"☆64Updated 4 years ago
- A Python package for building Bayesian models with TensorFlow or PyTorch☆176Updated 3 years ago
- Utilities to perform Uncertainty Quantification on Keras Models☆119Updated last year
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated 11 months ago
- A library for uncertainty quantification based on PyTorch☆122Updated 3 years ago
- Gaussian Process and Uncertainty Quantification Summer School 2020☆33Updated 2 years ago
- Exploring how to to deal with uncertain inputs with gaussian process regression models.☆27Updated 4 years ago
- Materials of the Nordic Probabilistic AI School 2021.☆93Updated 4 years ago
- Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model☆101Updated 10 months ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆100Updated 2 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Scalable Gaussian Process Regression with Derivatives☆38Updated 6 years ago
- PyHopper is a hyperparameter optimizer, made specifically for high-dimensional problems arising in machine learning research.☆87Updated last year
- A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)☆165Updated 4 years ago
- Bayesian neural networks via MCMC: tutorial☆58Updated 10 months ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 5 years ago
- Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of…☆70Updated last week