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pro vyhledávání: '"Nambi P"'
In the context of cellular networks, users located at the periphery of cells are particularly vulnerable to substantial interference from neighbouring cells, which can be represented as a two-user interference channel. This study introduces two highl
Externí odkaz:
http://arxiv.org/abs/2410.19767
Autor:
Singh, Namita, Wang'ombe, Jacqueline, Okanga, Nereah, Zelenska, Tetyana, Repishti, Jona, K, Jayasankar G, Mishra, Sanjeev, Manokaran, Rajsekar, Singh, Vineet, Rafiq, Mohammed Irfan, Gandhi, Rikin, Nambi, Akshay
Small and medium-sized agricultural holders face challenges like limited access to localized, timely information, impacting productivity and sustainability. Traditional extension services, which rely on in-person agents, struggle with scalability and
Externí odkaz:
http://arxiv.org/abs/2409.08916
We explore the use of FCNNs (Fully Connected Neural Networks) for designing end-to-end communication systems without taking any inspiration from existing classical communications models or error control coding. This work relies solely on the tools of
Externí odkaz:
http://arxiv.org/abs/2409.01129
Autor:
Channarayappa, Sharath Kumar, Kumar, Sankalp, Vidhyadhiraja, N. S., Pujari, Sumiran, Saravanan, M. P., Sebastian, Amal, Choi, Eun Sang, Chikara, Shalinee, Nambi, Dolly, Suresh, Athira, Lal, Siddhartha, Jaiswal-Nagar, D.
The ground state of a one-dimensional spin-1/2 uniform antiferromagnetic Heisenberg chain (AfHc) is a Tomonaga-Luttinger liquid which is quantum-critical with respect to applied magnetic fields upto a saturation field Hs beyond which it transforms to
Externí odkaz:
http://arxiv.org/abs/2408.11163
Autor:
Wu, Nemin, Cao, Qian, Wang, Zhangyu, Liu, Zeping, Qi, Yanlin, Zhang, Jielu, Ni, Joshua, Yao, Xiaobai, Ma, Hongxu, Mu, Lan, Ermon, Stefano, Ganu, Tanuja, Nambi, Akshay, Lao, Ni, Mai, Gengchen
Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, networks, images, etc.) in their native formats. Learning good spatial repres
Externí odkaz:
http://arxiv.org/abs/2406.15658
Autor:
Wang, Hengyi, Shi, Haizhou, Tan, Shiwei, Qin, Weiyi, Wang, Wenyuan, Zhang, Tunyu, Nambi, Akshay, Ganu, Tanuja, Wang, Hao
Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comprehensive evaluation of their long-context capabilities remains undere
Externí odkaz:
http://arxiv.org/abs/2406.11230
Large Language Models (LLMs) have been applied to Math Word Problems (MWPs) with transformative impacts, revolutionizing how these complex problems are approached and solved in various domains including educational settings. However, the evaluation o
Externí odkaz:
http://arxiv.org/abs/2406.10834
Autor:
Nambi, Marie Amalore, Kumar, Neeraj
An ideal $I$ of a commutative ring $R$ is said to be of linear type when its Rees algebra and symmetric algebra exhibit isomorphism. In this paper, we investigate the conjecture put forth by Jayanthan, Kumar, and Sarkar (2021) that if $G$ is a tree o
Externí odkaz:
http://arxiv.org/abs/2406.05960
Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering is both labor-intensive and domain-specific, necessitating the need fo
Externí odkaz:
http://arxiv.org/abs/2405.18369
Autor:
Kumar, Somnath, Balloli, Vaibhav, Ranjit, Mercy, Ahuja, Kabir, Ganu, Tanuja, Sitaram, Sunayana, Bali, Kalika, Nambi, Akshay
Large language models (LLMs) are at the forefront of transforming numerous domains globally. However, their inclusivity and effectiveness remain limited for non-Latin scripts and low-resource languages. This paper tackles the imperative challenge of
Externí odkaz:
http://arxiv.org/abs/2405.18359