Zobrazeno 1 - 10
of 1 031
pro vyhledávání: '"S, Sathiya"'
Publikováno v:
Journal of Medical Sciences and Health, Vol 10, Iss 2, Pp 127-135 (2024)
Introduction: The scapulae of human bone bears suprascapular notch and the ligament in it undergoes ossification causing compression of the suprascapular nerve. The degree of ossification of transverse ligament of scapula helps to differentiate it fr
Externí odkaz:
https://doaj.org/article/3f16d9ce6fa04a72988b3f0f5dd675b9
Publikováno v:
In Diamond & Related Materials August 2024 147
Autor:
Gupta, Aman, Ramanath, Rohan, Shi, Jun, Ramachandran, Anika, Zhou, Sirou, Zhou, Mingzhou, Keerthi, S. Sathiya
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems. Without appropriately tuned $\ell_2$ regularization or weight decay, such networks have the tendency to mak
Externí odkaz:
http://arxiv.org/abs/2108.05839
We consider applications involving a large set of instances of projecting points to polytopes. We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the p
Externí odkaz:
http://arxiv.org/abs/2103.05277
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$. Recently, Conditional GAN (CGAN) has been applied fo
Externí odkaz:
http://arxiv.org/abs/2003.01296
An average adult is exposed to hundreds of digital advertisements daily (https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf), making the digital advertisement industry a classic example of a big-data-driven platform. As
Externí odkaz:
http://arxiv.org/abs/2002.02879
Autor:
Aggarwal, Karan, Kirchmeyer, Matthieu, Yadav, Pranjul, Keerthi, S. Sathiya, Gallinari, Patrick
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in image proc
Externí odkaz:
http://arxiv.org/abs/1905.12868
We implement and evaluate deep learning for cardinality estimation by studying the accuracy, space and time trade-offs across several architectures. We find that simple deep learning models can learn cardinality estimations across a variety of datase
Externí odkaz:
http://arxiv.org/abs/1905.06425
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Deep reinforcement learning is quickly changing the field of artificial intelligence. These models are able to capture a high level understanding of their environment, enabling them to learn difficult dynamic tasks in a variety of domains. In the dat
Externí odkaz:
http://arxiv.org/abs/1803.08604