Creating and analyzing interaction graphs based on boolean functions
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Updated
Apr 13, 2016 - Java
Creating and analyzing interaction graphs based on boolean functions
Re-adapted implementation of Adversarial neural network model described in Learning to Protect Communications with Adversarial Neural Cryptography (Martín Abadi & David G. Andersen, 2016). The primary code is authored by Liam Schoneveld (https://nlml.github.io/neural-networks/adversarial-neural-cryptography/).
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A tiny library to help in exhaustive testing of Boolean functions in Python.
Implementation for my research project on local search for learning PaRtial bOOlean Functions.
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