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pro vyhledávání: '"Mhaskar AN"'
We say that a family $\mathcal{W}$ of strings over $\Sigma^+$ forms a Unique Maximal Factorization Family (UMFF) if and only if every $w \in \mathcal{W}$ has a unique maximal factorization. Further, an UMFF $\mathcal{W}$ is called a circ-UMFF wheneve
Externí odkaz:
http://arxiv.org/abs/2409.02757
Operator learning has emerged as a new paradigm for the data-driven approximation of nonlinear operators. Despite its empirical success, the theoretical underpinnings governing the conditions for efficient operator learning remain incomplete. The pre
Externí odkaz:
http://arxiv.org/abs/2405.15992
Motivated by a number of applications in signal processing, we study the following question. Given samples of a multidimensional signal of the form \begin{align*} f(\bs\ell)=\sum_{k=1}^K a_k\exp(-i\langle \bs\ell, \w_k\rangle), \\ \w_1,\cdots,\w_k\in
Externí odkaz:
http://arxiv.org/abs/2404.11004
Autor:
Mhaskar, Shivam Ratnakant, Shah, Nirmesh J., Zaki, Mohammadi, Gudmalwar, Ashishkumar P., Wasnik, Pankaj, Shah, Rajiv Ratn
Traditional Automatic Video Dubbing (AVD) pipeline consists of three key modules, namely, Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), and Text-to-Speech (TTS). Within AVD pipelines, isometric-NMT algorithms are employed to r
Externí odkaz:
http://arxiv.org/abs/2403.15469
Autor:
Mhaskar, H. N., O'Dowd, Ryan
Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. The manifold hypothesis assumes that the data is sampled from an unknown submanifold of a high dimensional Euclidean space.
Externí odkaz:
http://arxiv.org/abs/2402.12687
Motivated by applications in magnetic resonance relaxometry, we consider the following problem: Given samples of a function $t\mapsto \sum_{k=1}^K A_k\exp(-t\lambda_k)$, where $K\ge 2$ is an integer, $A_k\in\mathbb{R}$, $\lambda_k>0$ for $k=1,\cdots,
Externí odkaz:
http://arxiv.org/abs/2402.04348
Autor:
Mhaskar, Hrushikesh, Mao, Tong
In this paper, we present a sharper version of the results in the paper Dimension independent bounds for general shallow networks; Neural Networks, \textbf{123} (2020), 142-152. Let $\mathbb{X}$ and $\mathbb{Y}$ be compact metric spaces. We consider
Externí odkaz:
http://arxiv.org/abs/2308.03230
Autor:
Mhaskar, Shivam, Bhat, Vineet, Batheja, Akshay, Deoghare, Sourabh, Choudhary, Paramveer, Bhattacharyya, Pushpak
In this work, we present our deployment-ready Speech-to-Speech Machine Translation (SSMT) system for English-Hindi, English-Marathi, and Hindi-Marathi language pairs. We develop the SSMT system by cascading Automatic Speech Recognition (ASR), Disflue
Externí odkaz:
http://arxiv.org/abs/2305.12518
Autor:
Mhaskar, Hrushikesh
For the past 30 years or so, machine learning has stimulated a great deal of research in the study of approximation capabilities (expressive power) of a multitude of processes, such as approximation by shallow or deep neural networks, radial basis fu
Externí odkaz:
http://arxiv.org/abs/2305.03890
In many high-impact applications, it is important to ensure the quality of output of a machine learning algorithm as well as its reliability in comparison with the complexity of the algorithm used. In this paper, we have initiated a mathematically ri
Externí odkaz:
http://arxiv.org/abs/2303.00984