Theano implementations of thermodynamic Monte Carlo algorithms
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Mar 27, 2017 - Python
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hmc
Here are 64 public repositories matching this topic...
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Jan 16, 2018 - Stan
Used in Deep Machine Learning and Lattice Quantum Chromodynamics
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Jun 20, 2018 - Mathematica
Bayesian deep learning experiments
deep-learning jupyter-notebook pytorch dropout vi experiments reproducibility mcmc convolutional-neural-network variational-inference hmc pyro bayesian-deep-learning monte-carlo-dropout bayesian-neural-network bayesian-dl-experiments
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Jul 6, 2020 - Jupyter Notebook
An experimental Python package for learning Bayesian Neural Network.
deep-learning pytorch vi variational-inference hmc hamiltonian-monte-carlo pyro bayesian-deep-learning mc-dropout monte-carlo-dropout bayesian-neural-network
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Jul 6, 2020 - Python
How Bayesian should Bayesian Optimisation be?
inference bayesopt variational-inference bayesian-optimization hmc bayesian-optimisation acquisition-functions
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Jul 26, 2021 - Jupyter Notebook
R package that uses rstan capabilities to sample using `HMC' and inference for developmental rate dynamics of species in ecology
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Aug 26, 2021 - R
Modified TensorFlow implementation for training MCMC samplers on Lattice Gauge Theory models from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
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Oct 30, 2021 - Jupyter Notebook
Accompanying code for 'Manifold lifting: scaling MCMC to the vanishing noise regime'
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Aug 16, 2023 - Jupyter Notebook
Development of a Face Tracking Pipeline for lower face tracking RGB HMCs
vr hmd virtual-reality dataset-creation dataset-generation hmc head-mounted-display head-mounted-camera dataset-acquisition
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Oct 18, 2023 - Python
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
deep-learning reproducible-research regression pytorch uncertainty classification uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation mc-dropout bayes-by-backprop out-of-distribution-detection sgld
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Oct 20, 2023 - Jupyter Notebook
AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.
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Oct 23, 2023 - Python
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"
deep-learning uncertainty uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation bayes-by-backprop sgld
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Nov 1, 2023
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