Zobrazeno 1 - 10
of 180 653
pro vyhledávání: '"Expediting"'
Large language models (LLMs) have demonstrated remarkable capabilities in tasks requiring reasoning and multi-step problem-solving through the use of chain-of-thought (CoT) prompting. However, generating the full CoT process results in significantly
Externí odkaz:
http://arxiv.org/abs/2409.08561
Autor:
Azad, Bijan, author, King, Nelson, author
Publikováno v:
Organizing in the Digital Age : A Process View, 2024, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780198899457.003.0010
Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can r
Externí odkaz:
http://arxiv.org/abs/2407.00943
Recent advances in vision language pretraining (VLP) have been largely attributed to the large-scale data collected from the web. However, uncurated dataset contains weakly correlated image-text pairs, causing data inefficiency. To address the issue,
Externí odkaz:
http://arxiv.org/abs/2312.12659
Autor:
Sharmila, S.1 (AUTHOR), Saranya, A.1 (AUTHOR) saran.amrith@gmail.com, Arulprakasajothi, M.2 (AUTHOR), Saranya, R.3 (AUTHOR), Srimanickam, B.4 (AUTHOR), Abel, Sunil Kumar1 (AUTHOR), Shakeel, Faiyaz5 (AUTHOR), Faiyazuddin, Md6,7 (AUTHOR) md.faiyazuddin@gmail.com
Publikováno v:
BMC Chemistry. 10/19/2024, Vol. 18 Issue 1, p1-14. 14p.
Autor:
Heringer, Grace V.1 (AUTHOR), Vinson, David R.2,3,4 (AUTHOR) drvinson@ucdavis.edu
Publikováno v:
International Journal of Emergency Medicine. 10/8/2024, Vol. 17 Issue 1, p1-4. 4p.
Autor:
Zhen, Mengmeng1 (AUTHOR), Wang, Xiaoyu1 (AUTHOR), Yang, Qihang1 (AUTHOR), Zhang, Zihang1 (AUTHOR), Hu, Zhenzhong1 (AUTHOR) huzhenzhong@hebut.edu.cn, Li, Zhenyu2 (AUTHOR) zhenyu.li@swpu.edu.cn, Wang, Zhongchang3 (AUTHOR) zhongchangwang@buaa.edu.cn
Publikováno v:
Advanced Science. 9/25/2024, Vol. 11 Issue 36, p1-12. 12p.
First-order optimization (FOO) algorithms are pivotal in numerous computational domains such as machine learning and signal denoising. However, their application to complex tasks like neural network training often entails significant inefficiencies d
Externí odkaz:
http://arxiv.org/abs/2402.11427
In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors, can efficiently simulate the entire loading history of non-local gradient damage propagation. To
Externí odkaz:
http://arxiv.org/abs/2402.05460
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication
Externí odkaz:
http://arxiv.org/abs/2402.03815