Zobrazeno 1 - 10
of 217 464
pro vyhledávání: '"Sinha, A"'
Autor:
Sinha, Amarjeet
The Last Mile explores the gaps and dichotomy between drafted policies and their implementation, and the last mile challenges which often make public services inaccessible to the poorest and most vulnerable sections of society. It provides an in-dept
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/76425
We introduce a family of Helson matrices induced by the Laplace transform of a class of regular positive Borel measures $\mu,$ not necessarily finite, on $(0, \infty)$ and discuss their boundedness, Schatten-class properties and scattering theory. We
Externí odkaz:
http://arxiv.org/abs/2407.04124
Practical optimization problems may contain different kinds of difficulties that are often not tractable if one relies on a particular optimization method. Different optimization approaches offer different strengths that are good at tackling one or m
Externí odkaz:
http://arxiv.org/abs/2407.03454
Obtaining a precise form for the predicted gravitational wave (GW) spectrum from a phase transition is a topic of great relevance for beyond Standard Model (BSM) physicists. Currently, the most sophisticated semi-analytic framework for estimating the
Externí odkaz:
http://arxiv.org/abs/2407.02580
Autor:
Kornjača, Milan, Hu, Hong-Ye, Zhao, Chen, Wurtz, Jonathan, Weinberg, Phillip, Hamdan, Majd, Zhdanov, Andrii, Cantu, Sergio H., Zhou, Hengyun, Bravo, Rodrigo Araiza, Bagnall, Kevin, Basham, James I., Campo, Joseph, Choukri, Adam, DeAngelo, Robert, Frederick, Paige, Haines, David, Hammett, Julian, Hsu, Ning, Hu, Ming-Guang, Huber, Florian, Jepsen, Paul Niklas, Jia, Ningyuan, Karolyshyn, Thomas, Kwon, Minho, Long, John, Lopatin, Jonathan, Lukin, Alexander, Macrì, Tommaso, Marković, Ognjen, Martínez-Martínez, Luis A., Meng, Xianmei, Ostroumov, Evgeny, Paquette, David, Robinson, John, Rodriguez, Pedro Sales, Singh, Anshuman, Sinha, Nandan, Thoreen, Henry, Wan, Noel, Waxman-Lenz, Daniel, Wong, Tak, Wu, Kai-Hsin, Lopes, Pedro L. S., Boger, Yuval, Gemelke, Nathan, Kitagawa, Takuya, Keesling, Alexander, Gao, Xun, Bylinskii, Alexei, Yelin, Susanne F., Liu, Fangli, Wang, Sheng-Tao
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant res
Externí odkaz:
http://arxiv.org/abs/2407.02553
We consider a coupled atom-photon system described by the Tavis-Cummings dimer (two coupled cavities) in the presence of photon loss and atomic pumping, to investigate the quantum signature of dissipative chaos. The appropriate classical limit of the
Externí odkaz:
http://arxiv.org/abs/2407.00776
Autor:
Sinha, Ankur, Pankaj, Paritosh
In this paper, we formulate the hyperparameter tuning problem in machine learning as a bilevel program. The bilevel program is solved using a micro genetic algorithm that is enhanced with a linear program. While the genetic algorithm searches over di
Externí odkaz:
http://arxiv.org/abs/2407.00613
Safety classifiers are critical in mitigating toxicity on online forums such as social media and in chatbots. Still, they continue to be vulnerable to emergent, and often innumerable, adversarial attacks. Traditional automated adversarial data genera
Externí odkaz:
http://arxiv.org/abs/2406.17104
Autor:
Chua, Lynn, Ghazi, Badih, Huang, Yangsibo, Kamath, Pritish, Kumar, Ravi, Manurangsi, Pasin, Sinha, Amer, Xie, Chulin, Zhang, Chiyuan
Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2406.16135
Autor:
Chua, Lynn, Ghazi, Badih, Huang, Yangsibo, Kamath, Pritish, Kumar, Ravi, Liu, Daogao, Manurangsi, Pasin, Sinha, Amer, Zhang, Chiyuan
Large language models (LLMs) have emerged as powerful tools for tackling complex tasks across diverse domains, but they also raise privacy concerns when fine-tuned on sensitive data due to potential memorization. While differential privacy (DP) offer
Externí odkaz:
http://arxiv.org/abs/2406.14322