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Enhancing Bayesian Neural Networks with Synthetic and Real Data
This chapter explores data-driven strategies for selecting activation functions in Bayesian Neural Networks, utilizing insights from learned weights during training. By combining synthetic and real data, the chapter aims to enhance model accuracy and reliability while minimizing uncertainty.