activity
20182026
most citedUnnormalized Optimal Transport

59 citations · 73 across the 7 of their papers we have counts for

collaborators

9 papers

cs.LG2026

Motion Equivariant Convolutional LSTM: When Motion Becomes a Gauge Symmetry

Vahid Hosseinzadeh, T. Anderson Keller, Michael Puthawala

Objects move, cameras pan, and weather drifts with the wind: much of what changes in sequential data is motion. Motion changes where things are, not what they are, and is therefore…

math.CO2026

Maximal Equidistant Spacings

Michael Puthawala

We call a family in Euclidean space an equidistance spacing if whenever and . In other words, choosing…

cs.LG2024

Can neural operators always be continuously discretized?

Takashi Furuya, Michael Puthawala, Maarten V. de Hoop +1

We consider the problem of discretization of neural operators between Hilbert spaces in a general framework including skip connections. We focus on bijective neural operators throu…

cs.LG2023★ 1 cited

Globally injective and bijective neural operators

Takashi Furuya, Michael Puthawala, Matti Lassas +1

Recently there has been great interest in operator learning, where networks learn operators between function spaces from an essentially infinite-dimensional perspective. In this wo…

cs.LG2022★ 1 cited

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…

cs.LG2021★ 1 cited

Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows

Michael Puthawala, Matti Lassas, Ivan Dokmanić +1

We study approximation of probability measures supported on -dimensional manifolds embedded in by injective flows -- neural networks composed of invertible flows…

cs.LG2020★ 11 cited

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…

math.OC2019★ 59 cited

Unnormalized Optimal Transport

Wilfrid Gangbo, Wuchen Li, Stanley Osher +1

We propose an extension of the computational fluid mechanics approach to the Monge-Kantorovich mass transfer problem, which was developed by Benamou-Brenier. Our extension allows o…

math.NA2018

Diagnosing Forward Operator Error Using Optimal Transport

Michael A. Puthawala, Cory D. Hauck, Stanley J. Osher

We investigate overdetermined linear inverse problems for which the forward operator may not be given accurately. We introduce a new tool called the structure, based on the Wassers…