Zobrazeno 1 - 10
of 6 337
pro vyhledávání: '"P. Vemuri"'
Autor:
Naga Varaprasad Vemuri
Publikováno v:
Indian Journal of Radiology and Imaging, Vol 34, Iss 02, Pp 200-203 (2024)
Externí odkaz:
https://doaj.org/article/ea24df032d9b4532ae64db0a139e2464
Autor:
Chaurasia, Manish
Assuming that a function and its Fourier transform are dominated by Gaussians, a sharp estimate for the rate of exponential decay of its Hermite coefficients is obtained in terms of the variances of the dominating Gaussians.
Externí odkaz:
http://arxiv.org/abs/2312.02631
Autor:
Vemuri, Naga Varaprasad
Publikováno v:
The Indian Journal of Radiology & Imaging; April 2024, Vol. 34 Issue: 2 p200-203, 4p
Autor:
Rani, Pooja, Vemuri, M. K.
In [Pooja Rani and M. K. Vemuri, The Brylinski beta function of a double layer, Differential Geom. Appl. \textbf{92}(2024)], an analogue of Brylinski's knot beta function was defined for a compactly supported (Schwartz) distribution $T$ on Euclidean
Externí odkaz:
http://arxiv.org/abs/2410.00555
Autor:
Clendinning, Imogen
Publikováno v:
Journal of Environmental Media; April 2024, Vol. 5 Issue: 1 p161-165, 5p
Autor:
Mishra, Mansi, Vemuri, M. K.
If $T$ is a compactly supported distribution on $\mathbb{R}^{2n}$, then the Fourier transform of $T$ is $p$-th power integrable if and only if the Weyl transform of $T$ is $p$-th power traceable, and the Fourier transform of $T$ vanishes at infinity
Externí odkaz:
http://arxiv.org/abs/2409.16835
Physics-Informed Neural Networks (PINNs) have shown continuous and increasing promise in approximating partial differential equations (PDEs), although they remain constrained by the curse of dimensionality. In this paper, we propose a generalized PIN
Externí odkaz:
http://arxiv.org/abs/2408.13101
Autor:
Mishra, Mansi, Vemuri, M. K.
If $\mu$ is a smooth measure supported on a real-analytic submanifold of $\mathbb{R}^{2n}$ which is not contained in any affine hyperplane, then the Weyl transform of $\mu$ is a compact operator.
Externí odkaz:
http://arxiv.org/abs/2406.03128
How well do representations learned by ML models align with those of humans? Here, we consider concept representations learned by deep learning models and evaluate whether they show a fundamental behavioral signature of human concepts, the typicality
Externí odkaz:
http://arxiv.org/abs/2405.16128
Autor:
Chen, Zoey, Walsman, Aaron, Memmel, Marius, Mo, Kaichun, Fang, Alex, Vemuri, Karthikeya, Wu, Alan, Fox, Dieter, Gupta, Abhishek
Constructing simulation scenes that are both visually and physically realistic is a problem of practical interest in domains ranging from robotics to computer vision. This problem has become even more relevant as researchers wielding large data-hungr
Externí odkaz:
http://arxiv.org/abs/2405.11656