Before revision:  SSNs address this by learning where and how to share parameters between layers in a neural network while avoiding degenerate solutions that result in underfitting. Specifically, we automatically construct parameter groups that identify where parameter sharing is most beneficial. Then, we map each group's weights to construct layerswith learned combinations of candidates from a shared parameter pool. SSNs can share parameters across layers even when they have different sizes , perform different operations , and/or operate on features from different modalities .
After revision:  SSNs address this by learning where and how to allocate parameters to layers. This can result in sharing parameters across layers even when they have different sizes , perform different operations , and/or operate on features from different modalities .