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Humans are not only adept in recognizing what class an input instance belongsto (i.e., classification task), but perhaps more remarkably, they can imagine(i.e., generate) plausible instances of a desired class with ease, whenprompted. Inspired by this, we propose a framework which allows transformingCascade-Correlation Neural Networks (CCNNs) into probabilistic generativemodels, thereby enabling CCNNs to generate samples from a category of interest.CCNNs are a well-known class of deterministic, discriminative NNs, whichautonomously construct their topology, and have been successful in givingaccounts for a variety of psychological phenomena. Our proposed framework isbased on a Markov Chain Monte Carlo (MCMC) method, called theMetropolis-adjusted Langevin algorithm, which capitalizes on the gradientinformation of the target distribution to direct its explorations towardsregions of high probability, thereby achieving good mixing properties. Throughextensive simulations, we demonstrate the efficacy of our proposed framework.

Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models