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    <title>PyPI recent updates for deep-rewire</title>
    <link>https://pypi.org/project/deep-rewire/</link>
    <description>Recent updates to the Python Package Index for deep-rewire</description>
    <language>en</language>    <item>
      <title>1.0.5</title>
      <link>https://pypi.org/project/deep-rewire/1.0.5/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Thu, 27 Jun 2024 08:28:53 GMT</pubDate>
    </item>    <item>
      <title>1.0.4</title>
      <link>https://pypi.org/project/deep-rewire/1.0.4/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Tue, 25 Jun 2024 13:36:52 GMT</pubDate>
    </item>    <item>
      <title>1.0.3</title>
      <link>https://pypi.org/project/deep-rewire/1.0.3/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Mon, 24 Jun 2024 06:54:48 GMT</pubDate>
    </item>    <item>
      <title>1.0.2</title>
      <link>https://pypi.org/project/deep-rewire/1.0.2/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Mon, 24 Jun 2024 06:42:02 GMT</pubDate>
    </item>    <item>
      <title>1.0.1</title>
      <link>https://pypi.org/project/deep-rewire/1.0.1/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Mon, 24 Jun 2024 06:25:46 GMT</pubDate>
    </item>    <item>
      <title>1.0.0</title>
      <link>https://pypi.org/project/deep-rewire/1.0.0/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Fri, 21 Jun 2024 09:46:02 GMT</pubDate>
    </item>    <item>
      <title>0.1.0</title>
      <link>https://pypi.org/project/deep-rewire/0.1.0/</link>
      <description>DeepRewire is a PyTorch-based project designed to simplify the creation and optimization of sparse neural networks with the concepts from the [Deep Rewiring](https://arxiv.org/abs/1711.05136) paper by Bellec et. al. ⚠️ Note: The implementation is not made by any of the authors. Please double-check everything before use.</description>
<author>luggistruggi@gmail.com</author>      <pubDate>Fri, 21 Jun 2024 07:56:38 GMT</pubDate>
    </item>  </channel>
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