Pinned Repositories
Brancher
A user-centered Python package for differentiable probabilistic inference
AEF
Brancher
A python library for stochastic variational inference and differentiable probabilistic programming
CalculusTeachingMaterial
Material for teaching calculus in the Bachelor in AI at Radboud university.
CascadingFlow
Automatic structured variational inference with cascading flows
Functional-GP-analysis-in-Python
A python toolbox for GP analysis written in a functional programming style
GP-CaKe-project
Bayesian Effective Connectivity
LocallyCoupledGP
A flexible implementation of the locally coupled GP analysis
Probabilistic-Deep-Learning
Wasserstein-GAN-on-MNIST
This is a simple Chainer implementation of the Wasserstein GAN on MNIST digits.
LucaAmbrogioni's Repositories
LucaAmbrogioni/GP-CaKe-project
Bayesian Effective Connectivity
LucaAmbrogioni/CalculusTeachingMaterial
Material for teaching calculus in the Bachelor in AI at Radboud university.
LucaAmbrogioni/Brancher
A python library for stochastic variational inference and differentiable probabilistic programming
LucaAmbrogioni/Functional-GP-analysis-in-Python
A python toolbox for GP analysis written in a functional programming style
LucaAmbrogioni/CascadingFlow
Automatic structured variational inference with cascading flows
LucaAmbrogioni/Wasserstein-GAN-on-MNIST
This is a simple Chainer implementation of the Wasserstein GAN on MNIST digits.
LucaAmbrogioni/Probabilistic-Deep-Learning
LucaAmbrogioni/NeuralEnsembleForecaster
Sample based forecasting with Kernel Mixture Networks
LucaAmbrogioni/Brancher-1
A user-centered Python package for differentiable probabilistic inference
LucaAmbrogioni/LocallyCoupledGP
A flexible implementation of the locally coupled GP analysis
LucaAmbrogioni/TaylorAlgebra
A simple package for algebraic manipulation of Taylor series. It allows to sum, multiply, divide and compose Taylor series automatically.
LucaAmbrogioni/Brancher_chainer_backend
Brancher with Chainer backend. Brancher is a python library for stochastic variational inference and differentiable probabilistic programming. This version based on chainer is functional but it is not currently maintained.
LucaAmbrogioni/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
LucaAmbrogioni/v130
Proceedings of AISTATS 2021