Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
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Updated
Nov 29, 2017 - Python
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Implementations of the ICML 2017 paper (with Yarin Gal)
My undergraduate honours project, with others' private information/code removed.
Implementation of Sequential Attend, Infer, Repeat (SQAIR)
Approximate Ridge Linear Mixed Models (arLMM)
A curated list of resources about Machine Learning for Robotics
Probabilistic approach to neural nets - modern scalable approximate inference methods
Correcting predictions for approximate Bayesian inference
Variational Bayesian decision-making for continuous utilities
An implementation of loopy belief propagation for binary image denoising. Both sequential and parallel updates are implemented.
Denoise a given image using Loopy Belief Propagation
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
Empirical analysis of recent stochastic gradient methods for approximate inference in Bayesian deep learning, including SWA-Gaussian, MultiSWAG, and deep ensembles. See report_localglobal.pdf.
STOT: Single-Target Object Tracking using particle and Kalman filters [with a bonus multi-target].
Benchmark of posterior and model inference algorithms for (moderately) expensive likelihoods.
Code repository for the UAI 2020 paper "Active learning of conditional mean embeddings via Bayesian optimisation" by S. R. Chowdhury, R. Oliveira and F. Ramos.
Code repository for the paper No-Regret Approximate Inference via Bayesian Optimisation, published at UAI 2021
DGMs for NLP. A roadmap.
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