autobnn

v0.0.2.dev0

Package for training Gaussian process-like Bayesian Neural Networks with composite structure. For more information about how to use this package see README

Latest version published 9 months ago

License: Apache-2.0

PyPI

GitHub

Package Health Score

68 / 100

security
Security review needed
popularity
Small
maintenance
Inactive
community
Active

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Popularity

Small

GitHub Stars: 4.27K
Forks: 1.11K
Contributors: 360

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package autobnn, we found that it has been starred 4,272 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Mar 28, 2024
Direct Vulnerabilities: 0
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0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Inactive

Commit Frequency

Open Issues: 625
Open PR: 71
Last Release: 9 months ago
Last Commit: 17 days ago

Further analysis of the maintenance status of autobnn based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

We found that autobnn demonstrates a positive version release cadence with at least one new version released in the past 12 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 360
Funding: No

A good and healthy external contribution signal for autobnn project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.6

Age: 9 months
Latest Release: 9 months ago
Dependencies: N/A
Versions: 2
Maintainers: 0
Wheels: No

Readme

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:

Lassi Meronen, Martin Trapp, Arno Solin. Periodic Activation Functions Induce Stationarity. NeurIPS 2021.
Tim Pearce, Russell Tsuchida, Mohamed Zaki, Alexandra Brintrup, Andy Neely. Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions. UAI 2019.
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.

FAQs

What is autobnn?

Package for training Gaussian process-like Bayesian Neural Networks with composite structure. Visit Snyk Advisor to see a full health score report for autobnn, including popularity, security, maintenance & community analysis.

Is autobnn popular?

We’re still processing downloads data for the python package autobnn. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is autobnn well maintained?

We found indications that autobnn is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is autobnn safe to use?

While scanning the latest version of autobnn, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 21 December-2024, at 13:56 (UTC).

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