Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

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Deep LearningCMKN
Overview

CMKN

Implementation of the convolutional motif kernel network (CMKN) introduced in Ditz et al., "Convolutional Motif Kernel Network", 2021.

Testing

You can run the unit-tests by executing

$ python -m unittest

from the root of the project. The tests include some standard problems of pulse propagation in nonlinear media. During the tests an interactive plotter demonstrating the integration results will be shown. Unfortunately, at the moment it is not possible to disable it, so running tests in a headless setup is not straightforward.

Documentation

The documentation is written with sphinx. You can build it by running

$ cd doc && make html

from the root of the project. The entry point for the documentation will be placed in doc/_build/html/index.html which you can open with a browser of your choice.

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Releases(v0.1)

  • v0.1(Nov 15, 2021)

    This release is the first version of convolutional motif kernel networks. The code includes PyTorch objects to construct a CMKN model and handle input data. Furthermore, methods to analyze and interpret a trained CMKN model are included as well as routines to prepare HIVdb's data for experiments. A first documentation of the project can be build with sphinx.

    Source code(tar.gz)
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