Neural nets with implicit layers

Also, declarative networks

A unifying framework for various networks, including neural ODEs, where our layers are not simple forward operations but who exacluation is represented as some optimisation problem.

For some info see the NeurIPS 2020 tutorial, Deep Implicit Layers - Neural ODEs, Deep Equilibirum Models, and Beyond, by Zico Kolter, David Duvenaud, and Matt Johnson.

NB: This is different to the implicit representation method. Since implicit layers and implicit representation layers also occur in the same problems (such as ML PDES) this avoidable terminological confusion will haunt us.

To learn: connection to fixed point (Granas and Dugundji 2003) theory.

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.

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.


Adler, Jonas, and Ozan Öktem. 2018. “Learned Primal-Dual Reconstruction.” IEEE Transactions on Medical Imaging 37 (6): 1322–32.
Agrawal, Akshay, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and Zico Kolter. 2019. “Differentiable Convex Optimization Layers.” In Advances In Neural Information Processing Systems.
Amos, Brandon, and J. Zico Kolter. 2017. “OptNet: Differentiable Optimization as a Layer in Neural Networks,” March.
Amos, Brandon, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, and J. Zico Kolter. 2018. “Differentiable MPC for End-to-End Planning and Control,” October.
Andersson, Joel A. E., Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. 2019. “CasADi: A Software Framework for Nonlinear Optimization and Optimal Control.” Mathematical Programming Computation 11 (1): 1–36.
Arora, Sanjeev, Rong Ge, Tengyu Ma, and Ankur Moitra. 2015. “Simple, Efficient, and Neural Algorithms for Sparse Coding.” In Proceedings of The 28th Conference on Learning Theory, 40:113–49. Paris, France: PMLR.
Bai, Shaojie, J Zico Kolter, and Vladlen Koltun. 2019. “Deep Equilibrium Models.” In Advances in Neural Information Processing Systems, 32:12.
Bai, Shaojie, Vladlen Koltun, and J. Zico Kolter. 2020. “Multiscale Deep Equilibrium Models.” In Advances in Neural Information Processing Systems. Vol. 33.
———. 2021. “Stabilizing Equilibrium Models by Jacobian Regularization.” arXiv:2106.14342 [cs, Stat], June.
Banert, Sebastian, Jevgenija Rudzusika, Ozan Öktem, and Jonas Adler. 2021. “Accelerated Forward-Backward Optimization Using Deep Learning.” arXiv:2105.05210 [math], May.
Barratt, Shane. 2018. “On the Differentiability of the Solution to Convex Optimization Problems,” April.
Border, KC. 2019. “Notes on the Implicit Function Theorem.”
Borgerding, Mark, and Philip Schniter. 2016. “Onsager-Corrected Deep Networks for Sparse Linear Inverse Problems.” arXiv:1612.01183 [cs, Math], December.
Djolonga, Josip, and Andreas Krause. 2017. “Differentiable Learning of Submodular Models.” In Proceedings of the 31st International Conference on Neural Information Processing Systems, 1014–24. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.
Domke, Justin. 2012. “Generic Methods for Optimization-Based Modeling.” In International Conference on Artificial Intelligence and Statistics, 318–26.
Donti, Priya L., Brandon Amos, and J. Zico Kolter. 2017. “Task-Based End-to-End Model Learning in Stochastic Optimization,” March.
G.Krantz, Steven, and Harold R.Parks. 2002. The Implicit Function Theorem. Springer.
Gould, Stephen, Basura Fernando, Anoop Cherian, Peter Anderson, Rodrigo Santa Cruz, and Edison Guo. 2016. “On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-Level Optimization,” July.
Gould, Stephen, Richard Hartley, and Dylan Campbell. 2019. “Deep Declarative Networks: A New Hope,” September.
Granas, Andrzej, and James Dugundji. 2003. Fixed Point Theory. Springer Monographs in Mathematics. New York, NY: Springer New York.
Gregor, Karol, and Yann LeCun. 2010. “Learning fast approximations of sparse coding.” In Proceedings of the 27th International Conference on Machine Learning (ICML-10), 399–406.
———. 2011. “Efficient Learning of Sparse Invariant Representations.” arXiv:1105.5307 [cs], May.
Haber, Eldad, and Lars Ruthotto. 2018. “Stable Architectures for Deep Neural Networks.” Inverse Problems 34 (1): 014004.
Landry, Benoit, Joseph Lorenzetti, Zachary Manchester, and Marco Pavone. 2019. “Bilevel Optimization for Planning Through Contact: A Semidirect Method,” June.
Lee, Kwonjoon, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto. 2019. “Meta-Learning with Differentiable Convex Optimization,” April.
Mena, Gonzalo, David Belanger, Scott Linderman, and Jasper Snoek. 2018. “Learning Latent Permutations with Gumbel-Sinkhorn Networks,” February.
Poli, Michael, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park. 2020. “Hypersolvers: Toward Fast Continuous-Depth Models.” In Advances in Neural Information Processing Systems. Vol. 33.
Rajeswaran, Aravind, Chelsea Finn, Sham Kakade, and Sergey Levine. 2019. “Meta-Learning with Implicit Gradients,” September.
Sulam, Jeremias, Aviad Aberdam, Amir Beck, and Michael Elad. 2020. “On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (8): 1968–80.
Wang, Po-Wei, Priya L. Donti, Bryan Wilder, and Zico Kolter. 2019. “SATNet: Bridging Deep Learning and Logical Reasoning Using a Differentiable Satisfiability Solver,” May.

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