Deep Equilibrium Models

Understand

We present a new approach to modeling sequential data: the deep equilibrium model (DEQ).

• Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding.
• Such a method is equivalent to running an infinite depth (weight-tied) feedforward network, but has the notable advantage that we can analytically backpropagate through the equilibrium point using implicit differentiation.
• Using this approach, training and prediction in these networks require only constant memory, regardless of the effective "depth" of the network.