Zobrazeno 1 - 10
of 5 148
pro vyhledávání: '"Hayati P"'
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
Autor:
Anitha Dhanasekaran, Yathavan Subramanian, Lukman Ahmed Omeiza, Veena Raj, Hayati Pg Hj Md Yassin, Muhammed Ali SA, Abul K. Azad
Publikováno v:
Energies, Vol 16, Iss 1, p 208 (2022)
Protonic ceramic fuel cells (PCFCs) are one of the promising and emerging technologies for future energy generation. PCFCs are operated at intermediate temperatures (450–750 °C) and exhibit many advantages over traditional high-temperature oxygen-
Externí odkaz:
https://doaj.org/article/91c86d3f262a4f7dbe07a24774ebd8ac
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
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
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
Despite the Milky Way's proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being in dynamica
Externí odkaz:
http://arxiv.org/abs/2309.06476
We address the problem of synthesizing distorting mechanisms that maximize infinite horizon privacy for Networked Control Systems (NCSs). We consider stochastic LTI systems where information about the system state is obtained through noisy sensor mea
Externí odkaz:
http://arxiv.org/abs/2303.17519
Autor:
Lai, Bolin, Zhang, Hongxin, Liu, Miao, Pariani, Aryan, Ryan, Fiona, Jia, Wenqi, Hayati, Shirley Anugrah, Rehg, James M., Yang, Diyi
Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behav
Externí odkaz:
http://arxiv.org/abs/2212.08279