Zobrazeno 1 - 10
of 22 198
pro vyhledávání: '"Karthik. P"'
Autor:
Mohan, Karthik, Chen, Pengyu
In this paper, we describe our systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news is news that takes an extremely polarized political standpoint with an intention of cr
Externí odkaz:
http://arxiv.org/abs/2501.01370
This paper investigates the inherent trade-off between energy efficiency (EE) and spectral efficiency (SE) in distributed massive-MIMO (D-mMIMO) systems. Optimizing the EE and SE together is crucial as increasing spectral efficiency often leads to hi
Externí odkaz:
http://arxiv.org/abs/2501.01271
Autor:
Nair, Pradeep R., Raitani, Karthik
The commercialization prospects of perovskite light emitting diodes depend on its luminescence efficiency under large carrier densities. The decrease in luminescence efficiency under such high injection conditions could lead to an undesired increase
Externí odkaz:
http://arxiv.org/abs/2412.19572
Empirical Likelihood (EL) is a type of nonparametric likelihood that is useful in many statistical inference problems, including confidence region construction and $k$-sample problems. It enjoys some remarkable theoretical properties, notably Bartlet
Externí odkaz:
http://arxiv.org/abs/2412.18818
Frequency-bin encoding furnishes a compelling pathway for quantum information processing systems compatible with established lightwave infrastructures based on fiber-optic transmission and wavelength-division multiplexing. Yet although significant pr
Externí odkaz:
http://arxiv.org/abs/2412.17683
Autor:
Chen, Mingda, Li, Yang, Padthe, Karthik, Shao, Rulin, Sun, Alicia, Zettlemoyer, Luke, Gosh, Gargi, Yih, Wen-tau
Large language models can generate factually inaccurate content, a problem known as hallucination. Recent works have built upon retrieved-augmented generation to improve factuality through iterative prompting but these methods are limited by the trad
Externí odkaz:
http://arxiv.org/abs/2412.18069
Autor:
Vedula, Karthik S., Gupta, Annika, Swaminathan, Akshay, Lopez, Ivan, Bedi, Suhana, Shah, Nigam H.
Large language models (LLMs) excel at clinical information extraction but their computational demands limit practical deployment. Knowledge distillation--the process of transferring knowledge from larger to smaller models--offers a potential solution
Externí odkaz:
http://arxiv.org/abs/2501.00031
Autor:
Xu, Yi, Hu, Yuxin, Zhang, Zaiwei, Meyer, Gregory P., Mustikovela, Siva Karthik, Srinivasa, Siddhartha, Wolff, Eric M., Huang, Xin
Human drivers rely on commonsense reasoning to navigate diverse and dynamic real-world scenarios. Existing end-to-end (E2E) autonomous driving (AD) models are typically optimized to mimic driving patterns observed in data, without capturing the under
Externí odkaz:
http://arxiv.org/abs/2412.14446
Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry interaction i
Externí odkaz:
http://arxiv.org/abs/2412.13985
Autor:
Roh, Wonseok, Jung, Hwanhee, Kim, Jong Wook, Lee, Seunggwan, Yoo, Innfarn, Lugmayr, Andreas, Chi, Seunggeun, Ramani, Karthik, Kim, Sangpil
Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D scenes using finite resources. These approaches create a 3D radiance field, parameterized by per-pixel
Externí odkaz:
http://arxiv.org/abs/2412.12906