Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Balan, Radu"'
Consider a group $G$ of order $M$ acting unitarily on a real inner product space $V$. We show that the sorting based embedding obtained by applying a general linear map $\alpha : \mathbb{R}^{M \times N} \to \mathbb{R}^D$ to the invariant map $\beta_\
Externí odkaz:
http://arxiv.org/abs/2410.05446
Autor:
Balan, Radu, Jiang, Fushuai
A problem by Feichtinger, Heil, and Larson asks whether a positive-definite integral operator with $M_1$ kernel admits a symmetric rank-one decomposition which is strongly square-summable with respect to the $M_1$ norm. In conjunction with a concurre
Externí odkaz:
http://arxiv.org/abs/2409.20372
Autor:
Linehan, Kathryn, Balan, Radu
Computing the proximal operator of the $\ell_\infty$ norm, $\textbf{prox}_{\alpha ||\cdot||_\infty}(\mathbf{x})$, generally requires a sort of the input data, or at least a partial sort similar to quicksort. In order to avoid using a sort, we present
Externí odkaz:
http://arxiv.org/abs/2408.11211
Autor:
Balan, Radu, Tsoukanis, Efstratios
Consider a real vector space $\mathcal{V}$ and a finite group $G$ acting unitarily on $\mathcal{V}$. We study the general problem of constructing a stable embedding whose domain is the quotient of the vector space modulo the group action, and whose t
Externí odkaz:
http://arxiv.org/abs/2310.16365
Autor:
Balan, Radu, Tsoukanis, Efstratios
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 space of orbits. Our embedding is based on subsets of sorted coorbits.
Externí odkaz:
http://arxiv.org/abs/2308.11784
Autor:
Balan, Radu, Tsoukanis, Efstratios
This paper discusses the connection between the phase retrieval problem and permutation invariant embeddings. We show that the real phase retrieval problem for $\mathbb{R}^d/O(1)$ is equivalent to Euclidean embeddings of the quotient space $\mathbb{R
Externí odkaz:
http://arxiv.org/abs/2306.13111
Autor:
Xiao, Xiongye, Cao, Defu, Yang, Ruochen, Gupta, Gaurav, Liu, Gengshuo, Yin, Chenzhong, Balan, Radu, Bogdan, Paul
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 PDEs by learning the integral kernel directly in Fourier/Wavelet space
Externí odkaz:
http://arxiv.org/abs/2303.02304
Autor:
Zhang, Lei, Wang, Xiaoke, Rawson, Michael, Balan, Radu, Herskovits, Edward H., Melhem, Elias, Chang, Linda, Wang, Ze, Ernst, Thomas
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 combined with a two-stage multi-loss function. T1-weighted axial brai
Externí odkaz:
http://arxiv.org/abs/2210.14156
Normalizing flows provide an elegant approach to generative modeling that allows for efficient sampling and exact density evaluation of unknown data distributions. However, current techniques have significant limitations in their expressivity when th
Externí odkaz:
http://arxiv.org/abs/2203.11556
This paper presents primarily two Euclidean embeddings of the quotient space generated by matrices that are identified modulo arbitrary row permutations. The original application is in deep learning on graphs where the learning task is invariant to n
Externí odkaz:
http://arxiv.org/abs/2203.07546