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About the developer

AdamCobb
146 Stars 27 Forks BSD 2-Clause "Simplified" License 56 Commits 2 Opened issues

Description

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

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hamiltorch Build Status

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

  • Perform HMC in user-defined log probabilities and in PyTorch neural networks (objects inheriting from the
    torch.nn.Module
    ).
  • Available sampling schemes:
    • HMC
    • No-U-Turn Sampler (currently adapts step-size only)
    • Implicit RMHMC
    • Explicit RMHMC
    • Symmetric Split HMC

## How to install

pip install git+https://github.com/AdamCobb/hamiltorch

## How does it work?

There are currently two blog posts that describe how to use

hamiltorch
:

There are also notebook-style tutorials:

## How to cite?

Please consider citing the following papers if you use

hamiltorch
in your research:

For symmetric splitting:

@article{cobb2020scaling,
  title={Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting},
  author={Cobb, Adam D and Jalaian, Brian},
  journal={arXiv preprint arXiv:2010.06772},
  year={2020}
}

For RMHMC:

@article{cobb2019introducing,
  title={Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo},
  author={Cobb, Adam D and Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}} and Markham, Andrew and Roberts, Stephen J},
  journal={arXiv preprint arXiv:1910.06243},
  year={2019}
}

## Who developed hamiltorch?

Adam D Cobb

Atılım Güneş Baydin

Brian Jalaian

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