Neural nets with implicit layers

Optimization layers

Differentiable Convex Optimization Layers introduces cvxpylayers:

Optimization layers add domain-specific knowledge or learnable hard constraints to machine learning models.. Many of these layers solve convex and constrained optimization problems of the form

\[ \begin{array}{rl} x^{\star}(\theta)=\operatorname{argmin}_{x} & f(x ; \theta) \\ \text { subject to } g(x ; \theta) & \leq 0 \\ h(x ; \theta) & =0 \end{array} \]

with parameters θ, objective f, and constraint functions g,h and do end-to-end learning through them with respect to θ.

In this tutorial we introduce our new library cvxpylayers for easily creating differentiable new convex optimization layers. This lets you express your layer with the CVXPY domain specific language as usual and then export the CVXPY object to an efficient batched and differentiable layer with a single line of code. This project turns every convex optimization problem expressed in CVXPY into a differentiable layer.

Unrolling algorithms

The classic one here is Gregor and LeCun (2010), and a number of others related to thsi idea intermittently appear (Adler and Öktem 2018; Borgerding and Schniter 2016; Gregor and LeCun 2010; Sulam et al. 2020)

• Learning to reconstruct | Jonas Adler
• Accelerated Forward-Backward Optimization using Deep Learning | Jonas Adler

Deep declarative networks

A different terminology, although AFAICT closely related technology, is used by Stephen Gould in Gould, Hartley, and Campbell (2019), under the banner of Deep Declarative Networks. Fun applications he highlights: robust losses in pooling layers, projection onto shapes, convex programming and warping, matching problems, (relaxed) graph alignment, noisy point-cloud surface reconstruction… (I am sitting in his seminar as I write this.) They implemented a ddn library (pytorch).

To folow up from that presentation: Learning basis decomposition, hyperparameter optimisation… Stephen relates these to Deep declarative by discussing both problems as “bi-level optimisation problems”. Also discusses some minimax-like optimisations to “Stackelberg games” which are an optimisation problem embedded in game theory.

Deep equilibrium networks

Related: Deep equilibrium networks (Bai, Kolter, and Koltun 2019; Bai, Koltun, and Kolter 2020). In this one we assume that the network has a single layer which is iterated, and then solve for a fixed point of that iterated layer; this turns out to be memory efficient and in fact powerful (you need to scale up the width of that magic layer up to make it match the effective depth of a non-iterative layer stack, but not so very much.)

Example code: locuslab/deq.

References