CPNNs
CPNNs, or Convolutional Pseudo-Boolean Neural Networks, are a type of deep learning model designed to process data structured as a grid or image, while simultaneously handling discrete, symbolic variables. Unlike traditional Convolutional Neural Networks (CNNs) that operate on continuous pixel values, CPNNs are specifically built to work with Pseudo-Boolean variables, which can only take on values of 0 or 1. This makes them suitable for tasks involving logical operations, combinatorial optimization, and rule-based systems.
The core idea behind CPNNs is to adapt the convolutional mechanism to operate on binary or discrete
CPNNs find applications in areas such as constraint satisfaction problems, where the goal is to find assignments