 To address this issue, we present Shapeshifter Networks (SSNs), a flexible neural network framework that decouples layers from model weights, enabling us to implement any neural network with an arbitrary number of parameters . In SSNseach layer obtains weights from a parameter store that decides where and how to allocate parameters to layers .
<meaning-changed>  To address this issue, we present Shapeshifter Networks (SSNs), a flexible neural network framework that decouples layers from model weights, enabling us to implement any neural network with an arbitrary number of parameters . In SSNseach layer obtains weights from a parameter store that decides where and how to allocate parameters to layers .
 Parameter sharing can reduce memory requirements, but existing methods only share parameters between identical layers, limiting their impact. This paper removes these restrictions with a novel task called Neural Parameter Allocation Search (NPAS), where the goal is to generate weights for a network using a given parameter budget. NPAS requires new techniques to morph available parameters to fit any architecture. To address this new task we introduce Shapeshifter Networks (SSNs), which automatically learns where and how to allocate parameters to layers .