ICLR 2020 (spotlight)
Authors: Marin Vlastelica*, Anselm Paulus*, Vít Musil, Georg Martius, Michal Rolínek
Embed blackbox combinatorial solvers into neural networks without any sacrifices! How to turn c++ code for solving e.g. travelling salesman into a differentiable NN building block. Mostly theoretical work with update rule resembling classical SVM-based methods but revamped to make good theoretical sense for deep learning. The start of #blackboxbackprop.
This paper was among the top rated papers at OpenReview for the entire ICLR 2020 conference.
Bridging discrete optimization with deep learning. Maybe highly optimized (nondifferentiable) solvers for combinatorial problems will become great again 😍.— Michal Rolínek (@MichalRolinek) December 6, 2019
Differentiation of Blackbox Combinatorial Solvers https://t.co/YOzYOYhz20 pic.twitter.com/N9v6PprYGg