619 citations · 1.4k across the 27 of their papers we have counts for
30 papers
Joint Cryo-ET Alignment and Reconstruction with Neural Deformation Fields
Valentin Debarnot, Sidharth Gupta, Konik Kothari +1
We propose a framework to jointly determine the deformation parameters and reconstruct the unknown volume in electron cryotomography (CryoET). CryoET aims to reconstruct three-dime…
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…
On Procrustes Analysis in Hyperbolic Space
Puoya Tabaghi, Ivan Dokmanic
Congruent Procrustes analysis aims to find the best matching between two point sets through rotation, reflection and translation. We formulate the Procrustes problem for hyperbolic…
Truly shift-invariant convolutional neural networks
Anadi Chaman, Ivan Dokmanić
Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the out…
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…