Please explain how Hopfield network influenced modern deep learning models based on supervised differentiable training. All the "impactful" architectures such as MLP, CNN, Attention, come from a completely different paradigm, a paradigm that could be more straightforwardly connected to optimization theory.
They did not bring it into existence. The MLP is older than the Hopfield network. The invention that made it practical was back propagation, which wasn't used here at all.
