If we want to manage references or citations in our blog, the Pelican-cite plugin makes it simple. Here’s how to set it up.

Installation

Begin by installing the pelican-cite dependency pybtex with pip, followed by cloning the plugin into our project directory.

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pip install pybtex
git clone https://github.com/VorpalBlade/pelican-cite plugins/pelican-cite

Configuration

Next, configure the pelican settings file pelicanconf.py to activate the plugin and specify our bibliography source file.

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PLUGIN_PATHS = ["plugins"]  # Directory where Pelican looks for plugins
PLUGINS = ["pelican-cite", "render_math", "pelican-toc"]  # List of active plugins
PUBLICATIONS_SRC = "content/reference.bib"  # Path to the bibliography file
BIBLIOGRAPHY_START = '<section id="references"><h2>References</h2>'  # To change the default name Biblography to References
BIBLIOGRAPHY_END = '</section>'  # HTML end tag for references section

Create a Bibliography File

Create a BibTeX file (e.g., content/reference.bib) with our references. The path should match the PUBLICATIONS_SRC setting.

To given an example, I will show the references used in the ROAR (Remove and Retrain) repository built for ranking different interpretability methods:

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@inproceedings{khakzar2021criticalpathways,
  title={Neural Response Interpretation through the Lens of Critical Pathways},
  author={Khakzar, Ashkan and others},
  booktitle={CVPR},
  year={2021}
}

@inproceedings{hooker2019benchmark,
  title={A Benchmark for Interpretability Methods in Deep Neural Networks},
  author={Hooker, Sara and others},
  booktitle={NeurIPS},
  year={2019}
}

@article{samek2017evaluating,
  title={Evaluating the Visualization of What a Deep Neural Network Has Learned},
  author={Samek, Wojciech and others},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2017}
}

Use Citations in Posts

Then, inside our markdown post, you can cite them like this:

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Deep learning interpretation methods are an active research area 
[@khakzar2021criticalpathways], [@hooker2019benchmark], [@samek2017evaluating].

And this is how it will appear in blog:

Deep learning interpretation methods are an active research area Khakzar and et al. (2021), Hooker and et al. (2019), Samek and et al. (2017).

Pelican will automatically generate a References section at the end of the article using the pelican-cite plugin.

References

Sara Hooker and others. A benchmark for interpretability methods in deep neural networks. In NeurIPS. 2019. ↩

Ashkan Khakzar and others. Neural response interpretation through the lens of critical pathways. In CVPR. 2021. ↩

Wojciech Samek and others. Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems, 2017. ↩