16 citations · 25 across the 6 of their papers we have counts for
3 papers · 1 filter
Deep Invertible Approximation of Topologically Rich Maps between Manifolds
Michael Puthawala, Matti Lassas, Ivan Dokmanic +2
How can we design neural networks that allow for stable universal approximation of maps between topologically interesting manifolds? The answer is with a coordinate projection. Neu…
The fastest prox in the west
Benjamín Béjar, Ivan Dokmanić, René Vidal
Proximal operators are of particular interest in optimization problems dealing with non-smooth objectives because in many practical cases they lead to optimization algorithms whose…
Don't take it lightly: Phasing optical random projections with unknown operators
Sidharth Gupta, Rémi Gribonval, Laurent Daudet +1
In this paper we tackle the problem of recovering the phase of complex linear measurements when only magnitude information is available and we control the input. We are motivated b…