Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
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Updated
Oct 20, 2023 - Jupyter Notebook
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
DGMs for NLP. A roadmap.
A curated list of resources about Machine Learning for Robotics
Implementation of Sequential Attend, Infer, Repeat (SQAIR)
Probabilistic Programming with Gaussian processes in Julia
Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
Implementations of the ICML 2017 paper (with Yarin Gal)
A Python package for approximate Bayesian inference and optimization using Gaussian processes
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Input Inference for Control (i2c), a control-as-inference framework for optimal control
Benchmark of posterior and model inference algorithms for (moderately) expensive likelihoods.
Variational Bayesian decision-making for continuous utilities
A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"
Approximate Ridge Linear Mixed Models (arLMM)
FAIKR MOD3 project
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
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