Zobrazeno 1 - 10
of 21 975
pro vyhledávání: '"A. Hayati"'
Publikováno v:
International Journal of Human Capital in Urban Management, Vol 9, Iss 4, Pp 601-616 (2024)
BACKGROUND AND OBJECTIVES: This study investigates the intricate connections that exist between place attachment, urban development meanings, and acceptance in urban peripheral settlements. The research aims to gain a better understanding of how peop
Externí odkaz:
https://doaj.org/article/cf12ef9c1ff040da9b2f04531d65508c
Autor:
Akhlaq, Filza, Arshad, Alina, Hayati, Muhammad Yehya, Shamsi, Jawwad A., Khan, Muhammad Burhan
Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptabl
Externí odkaz:
http://arxiv.org/abs/2411.15773
Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. Howe
Externí odkaz:
http://arxiv.org/abs/2409.17201
Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF). Unlike humans, whose decisions and conf
Externí odkaz:
http://arxiv.org/abs/2404.09127
Publikováno v:
خانواده و پژوهش, Vol 15, Iss 3, Pp 127-146 (2018)
Children’s questioning is the beginning of awareness and thinking and the family plays a significant role in its development. Negative or neutral attitude towards this issue hinders children’s questioning. This study aimed to identify family barr
Externí odkaz:
https://doaj.org/article/19f6579919274356bf69293674c8e49a
Cloud computing enables users to process and store data remotely on high-performance computers and servers by sharing data over the Internet. However, transferring data to clouds causes unavoidable privacy concerns. Here, we present a synthesis frame
Externí odkaz:
http://arxiv.org/abs/2403.04485
Autor:
Hayati, Shirley Anugrah, Jung, Taehee, Bodding-Long, Tristan, Kar, Sudipta, Sethy, Abhinav, Kim, Joo-Kyung, Kang, Dongyeop
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. However, most existing instruction datasets include only single instructio
Externí odkaz:
http://arxiv.org/abs/2402.11532
Autor:
Das, Debarati, De Langis, Karin, Martin-Boyle, Anna, Kim, Jaehyung, Lee, Minhwa, Kim, Zae Myung, Hayati, Shirley Anugrah, Owan, Risako, Hu, Bin, Parkar, Ritik, Koo, Ryan, Park, Jonginn, Tyagi, Aahan, Ferland, Libby, Roy, Sanjali, Liu, Vincent, Kang, Dongyeop
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including annotations, preferences, instruction prompts, simulated dialogues, and f
Externí odkaz:
http://arxiv.org/abs/2401.14698
Publikováno v:
Journal of Applied Research in Higher Education, 2023, Vol. 16, Issue 5, pp. 1564-1583.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JARHE-02-2023-0059
Collecting diverse human opinions is costly and challenging. This leads to a recent trend in exploiting large language models (LLMs) for generating diverse data for potential scalable and efficient solutions. However, the extent to which LLMs can gen
Externí odkaz:
http://arxiv.org/abs/2311.09799