# Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models

@article{Nobandegani2017ConvertingCN, title={Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models}, author={Ardavan Salehi Nobandegani and Thomas R. Shultz}, journal={ArXiv}, year={2017}, volume={abs/1701.05004} }

Humans are not only adept in recognizing what class an input instance belongs to (i.e., classification task), but perhaps more remarkably, they can imagine (i.e., generate) plausible instances of a desired class with ease, when prompted. Inspired by this, we propose a framework which allows transforming Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative models, thereby enabling CCNNs to generate samples from a category of interest. CCNNs are a well-known class of…

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