ebonilla Goto Github PK

agp

Automated Variational Inference for Gaussian Process Models

autogp

Code for AutoGP

bayes_dag

Project Implemeting BayesDag of Annadani et al

bayestrack

Bayesian Approaches to State Estimation and Tracking in Multi-Scale Systems

bgcn

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

cogp

Collaborative multi-output Gaussian processes

convolutional_deep_gp_random_features

Implementation of paper Calibrating Deep Convolutional Gaussian Processes

deep_gp_random_features

differentiable-dag-sampling

Differentiable DAG Sampling (ICLR 2022)

dmm

Deep Markov Models

dppl

Discriminative Probabilistic Prototype Learning

ebonilla.github.io

My Home Page

ebonilla.github.io.old

Edwin V. Bonilla's Personal Web Page

fastgcn

The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""

ffvd

Code repository for Free-Form Variational Inference for Gaussian Process State-Space Models (ICML-2023)

fgp

Code for the paper 'Fast Allocation of Gaussian Process Experts'

filterpy

Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

gaussianprocesses

Modern Gaussian Processes: Scalable Inference and Novel Applications

ggp

Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'

gp-dre

Gaussian Process Density Ratio Estimation

gp-kitchen-sinks

Code for Extended and Unscented Kitchen Sinks

gpflow

Gaussian processes in TensorFlow

gplatnet

gppe

Gaussian Process Elicitation Framework

gprn

Code for the paper 'Efficient Variational Inference for Gaussian Process Regression Networks'

gpt

Gaussian Processes for Sequential Data

graph-representation-learning-tutorial

Code for Data61's tutorial on Graph Representation Learning

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.

linearizedgp

Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.

mcpm

Code for MCPM