Weight clamping as implicit network architecture definition • r/MachineLearning

I've been wondering some things about various neural network architectures and I have a question. Can all neural network architectures (recurrent, convolutional, GAN etc.) be described simply as a computational graph with fully connected layers where a subset of the trainable weights are clamped together (ie. Is there something missing in this description? Lots of different deep learning papers go on to great lengths to describe some sort of new neural network architecture and at a first glance, the differences can seem really huge. Some of the architectures seem to be only applicable to some domains and inherently, different than others.