Before revision:  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 can share parameters across layers even when they have different sizes , perform different operations . SSNs do not require any modifications to a model's loss function or architecture, making them easy to use. Our approach can create parameter efficient networks by using a relatively small number of weights, or can improve a model's performance by adding additional model capacity during training without affecting the computational resources required at test time .