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[ci skip] Merge pull request #64 from vyron-arvanitis/release/pypi-sklearn-contrib-bde
Release/pypi sklearn contrib bde cddb7e9
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‎.buildinfo‎

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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 7f781be20d93321a967a6a433c5c9a70
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config: 52d56eefd4959465593413c66c782927
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tags: 645f666f9bcd5a90fca523b33c5a78b7

‎_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip‎

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‎_sources/index.rst.txt‎

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.. bde documentation master file, created by
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.. bde documentation master file, created by
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sphinx-quickstart on Mon Jan 18 14:44:12 2016.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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**Date**: |today| **Version**: |version|
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**Useful links**:
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`Source Repository <https://github.com/vyron-arvanitis/bde.git>`__
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`Source Repository <https://github.com/scikit-learn-contrib/bde.git>`__ |
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`Issues & Ideas <https://github.com/scikit-learn-contrib/bdek/issues>`__
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.. `Source Repository <https://github.com/scikit-learn-contrib/bde>`__ |
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.. `Issues & Ideas <https://github.com/scikit-learn-contrib/bdek/issues>`__ |
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This is the documentation for the `bde`.
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This is the documentation for the `bde` package.
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‎_sources/quick_start.rst.txt‎

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.. _quick_start:
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.. _quick_start:
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###############
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Quick start
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Installation
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============
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The package will be published on PyPI soon. Until then you can install the
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current source build:
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To install the latest release from PyPI, run:
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.. prompt:: bash $
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pip install git+https://github.com/vyron-arvanitis/bde.git
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pip install sklearn-contrib-bde
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To install the latest development version from GitHub, run:
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.. prompt:: bash $
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pip install git+https://github.com/scikit-learn-contrib/bde.git
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If you prefer reproducible environments with GPU or multi-device CPU support,
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consider using `pixi <https://prefix.dev/docs/pixi/overview>`__ as described in

‎_sources/user_guide.rst.txt‎

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encountered in neural networks and can be implemented embarrassingly parallel across
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multiple devices.
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Note that this package currently supports fully connected feedforward networks only and
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is targeted at tabular data tasks. The method can however also be applied to other
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architectures and data modalities, but these are not yet in the scope of this
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particular implementation. Further the full package acts in the full-batch setting
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only; stochastic mini-batching is not supported at this time.
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For references and theoretical as well as algorithmic details, see
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`Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks (ICLR 2025) <https://arxiv.org/abs/2502.06335>`_.
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‎_static/documentation_options.js‎

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const DOCUMENTATION_OPTIONS = {
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VERSION: '0.1.dev339+g14caffd3e',
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VERSION: '1.0.1.dev7+gcddb7e916',
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LANGUAGE: 'en',
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‎api.html‎

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<a href="https://github.com/scikit-learn-contrib/bde" title="GitHub" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i></span>
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‎auto_examples/example.html‎

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<a href="https://github.com/scikit-learn-contrib/bde" title="GitHub" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i></span>
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<a href="https://github.com/scikit-learn-contrib/bde" title="GitHub" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i></span>
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<a href="https://github.com/scikit-learn-contrib/bde" title="GitHub" class="nav-link" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><span><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i></span>
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