15 citations · 56 across the 12 of their papers we have counts for
6 papers · 1 filter
Learning Multiscale Convolutional Dictionaries for Image Reconstruction
Tianlin Liu, Anadi Chaman, David Belius +1
Convolutional neural networks (CNNs) have been tremendously successful in solving imaging inverse problems. To understand their success, an effective strategy is to construct simpl…
Geometry of Similarity Comparisons
Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic +1
Many data analysis problems can be cast as distance geometry problems in \emph{space forms} -- Euclidean, spherical, or hyperbolic spaces. Often, absolute distance measurements are…
Globally Injective ReLU Networks
Michael Puthawala, Konik Kothari, Matti Lassas +2
Injectivity plays an important role in generative models where it enables inference; in inverse problems and compressed sensing with generative priors it is a precursor to well pos…
Learning the geometry of wave-based imaging
Konik Kothari, Maarten de Hoop, Ivan Dokmanić
We propose a general physics-based deep learning architecture for wave-based imaging problems. A key difficulty in imaging problems with a varying background wave speed is that the…
Hyperbolic Distance Matrices
Puoya Tabaghi, Ivan Dokmanić
Hyperbolic space is a natural setting for mining and visualizing data with hierarchical structure. In order to compute a hyperbolic embedding from comparison or similarity informat…
3D unknown view tomography via rotation invariants
Mona Zehni, Shuai Huang, Ivan Dokmanić +1
In this paper, we study the problem of reconstructing a 3D point source model from a set of 2D projections at unknown view angles. Our method obviates the need to recover the proje…