JIMIMVP.GITHUB.IO

As a continuation of the blackbox-backprop line of work, we introduce a simple modification to the algorith, namely treating the solver as an identity mapping in the computation graph on the backward pass. This, coupled with projections that avoid degenerate cases, works comparably well as ... Problems that are inherently combinatorial still remain a hinderance for classical deep learning methods. Traditional methods that try to do gradient propagation through combinatorial solvers rely on sample-based estimates or solver relaxations. We show that for a specific class of solver, we are able to efficiently compute gradients of an implicit piecewise-linear interpolation of the objective. This allows us to achieve unprecedented generalization performance on representation learning tasks with combinatorial flavor.