1 code implementation • 4 Mar 2023 • Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
1 code implementation • 19 Oct 2022 • Lei Zhang, Xiaoke Wang, Michael Rawson, Radu Balan, Edward H. Herskovits, Elias Melhem, Linda Chang, Ze Wang, Thomas Ernst
Evaluation used simulated T1 and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans.
no code implementations • 22 Mar 2022 • Sahil Sidheekh, Chris B. Dock, Tushar Jain, Radu Balan, Maneesh K. Singh
Normalizing flows provide an elegant approach to generative modeling that allows for efficient sampling and exact density evaluation of unknown data distributions.
no code implementations • 14 Mar 2022 • Radu Balan, Naveed Haghani, Maneesh Singh
In turn, this proves that almost any classifier can be implemented with an arbitrary small loss of performance.
no code implementations • 2 Dec 2021 • Michael Rawson, Radu Balan
We show an error or regret bound and convergence of the Deep Epsilon Greedy method which chooses actions with a neural network's prediction.
no code implementations • ICLR 2022 • Gaurav Gupta, Xiongye Xiao, Radu Balan, Paul Bogdan
The Padé exponential operator uses a $\textit{recurrent structure with shared parameters}$ to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession.
4 code implementations • 20 Apr 2021 • Andrew Lauziere, Ryan Christensen, Hari Shroff, Radu Balan
Finding an optimal correspondence between point sets is a common task in computer vision.
no code implementations • 4 Aug 2018 • Dongmian Zou, Radu Balan, Maneesh Singh
Many convolutional neural networks (CNNs) have a feed-forward structure.
no code implementations • 3 Nov 2017 • Addison Bohannon, Brian Sadler, Radu Balan
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations.
no code implementations • 18 Jan 2017 • Radu Balan, Maneesh Singh, Dongmian Zou
In this paper we discuss the stability properties of convolutional neural networks.
no code implementations • 10 Mar 2014 • Radu Balan, Dongmian Zou
In this note we prove that reconstruction from magnitudes of frame coefficients (the so called "phase retrieval problem") can be performed using Lipschitz continuous maps.
no code implementations • 25 Aug 2013 • Radu Balan
In this paper we study the property of phase retrievability by redundant sysems of vectors under perturbations of the frame set.
no code implementations • 21 Aug 2013 • Radu Balan, Yang Wang
This paper is concerned with the question of reconstructing a vector in a finite-dimensional real Hilbert space when only the magnitudes of the coefficients of the vector under a redundant linear map are known.