47 citations · 222 across the 30 of their papers we have counts for
31 papers
G-Invariant Representations using Coorbits: Bi-Lipschitz Properties
Radu Balan, Efstratios Tsoukanis
Consider a finite dimensional real vector space and a finite group acting unitarily on it. We study the general problem of constructing Euclidean stable embeddings of the quotient…
Relationships between the Phase Retrieval Problem and Permutation Invariant Embeddings
Radu Balan, Efstratios Tsoukanis
This paper discusses the connection between the phase retrieval problem and permutation invariant embeddings. We show that the real phase retrieval problem for …
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Xiongye Xiao, Defu Cao, Ruochen Yang +5
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve…
Motion correction in MRI using deep learning and a novel hybrid loss function
Lei Zhang, Xiaoke Wang, Michael Rawson +6
Purpose To develop and evaluate a deep learning-based method (MC-Net) to suppress motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived from a UNet…
Permutation Invariant Representations with Applications to Graph Deep Learning
Radu Balan, Naveed Haghani, Maneesh Singh
This paper presents primarily two Euclidean embeddings of the quotient space generated by matrices that are identified modulo arbitrary row permutations. The original application i…
VQ-Flows: Vector Quantized Local Normalizing Flows
Sahil Sidheekh, Chris B. Dock, Tushar Jain +2
Normalizing flows provide an elegant approach to generative modeling that allows for efficient sampling and exact density evaluation of unknown data distributions. However, current…