Log In Sign Up

Deep equilibrium networks are sensitive to initialization statistics

by   Atish Agarwala, et al.

Deep equilibrium networks (DEQs) are a promising way to construct models which trade off memory for compute. However, theoretical understanding of these models is still lacking compared to traditional networks, in part because of the repeated application of a single set of weights. We show that DEQs are sensitive to the higher order statistics of the matrix families from which they are initialized. In particular, initializing with orthogonal or symmetric matrices allows for greater stability in training. This gives us a practical prescription for initializations which allow for training with a broader range of initial weight scales.


page 1

page 2

page 3

page 4


Backward induction for repeated games

We present a method of backward induction for computing approximate subg...

Mean Shift Rejection: Training Deep Neural Networks Without Minibatch Statistics or Normalization

Deep convolutional neural networks are known to be unstable during train...

Symmetric equilibrium of multi-agent reinforcement learning in repeated prisoner's dilemma

We investigate the repeated prisoner's dilemma game where both players a...

On the limit behavior of iterated equilibrium distributions for the Gamma and Weibull families

In this paper, we study the evolution of iterated equilibrium distributi...

Higher-order recurrence relations, Sobolev-type inner products and matrix factorizations

It is well known that Sobolev-type orthogonal polynomials with respect t...

Global Convergence of Over-parameterized Deep Equilibrium Models

A deep equilibrium model (DEQ) is implicitly defined through an equilibr...