Speaker: Mario Franco

Abstract:
A prevailing design principle of deep neural networks is stacking the same module of neural architectures on top of each other. Despite being widely accepted as a universal principle, its validity and effectiveness has been rarely questioned. Here, we question this common design principle by demonstrating that neural networks with composite activation functions demonstrate a robust, improved learning compared to that with a single type.