This release is a pre-release and may not be stable for production use.
AutoBNN
This library contains code to specify BNNs that correspond to various useful GP kernels and assemble them into models using operators such as Addition, Multiplication and Changepoint.
It is based on the ideas in the following papers:
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Lassi Meronen, Martin Trapp, Arno Solin. Periodic Activation Functions Induce Stationarity. NeurIPS 2021.
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Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely. Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions. UAI 2019.
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Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka. Sequential Monte Carlo Learning for Time Series Structure Discovery. ICML 2023.
Setup
AutoBNN has three additional dependencies beyond those used by the core
Tensorflow Probability package: flax, scipy and jaxtyping. These
can be installed by running setup_autobnn.sh.
1 maintainer
Release files for autobnn 0.0.2.dev0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| autobnn-0.0.2.dev0.tar.gz | 29.4 kB | Details |
Release files / autobnn-0.0.2.dev0.tar.gz
| Download URL | autobnn-0.0.2.dev0.tar.gz |
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| Size | 29.4 kB |
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