A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks

NeurIPS 2017 Qinliang SuXuejun LiaoLawrence Carin

We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used nonlinearities in neural networks... (read more)

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