Results 291 to 300 of about 8,464,096 (315)

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

arXiv.org
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks, it is necessary to fine-tune LLMs using private data.
Zihan Fang, Zheng Lin, Zhe Chen, Xianhao Chen, Yue Gao, Yuguang Fang   +5 more
semanticscholar   +1 more source

Choosing a robustness tuning parameter

Journal of Statistical Computation and Simulation, 2005
A novel method is proposed for choosing the tuning parameter associated with a family of robust estimators. It consists of minimising estimated mean squared error, an approach that requires pilot estimation of model parameters. The method is explored for the family of minimum distance estimators proposed by [Basu, A., Harris, I.R., Hjort, N.L.
J. Warwick, M. C. Jones
openaire   +1 more source

Scalable parameter tuning for AVQ

IEEE Communications Letters, 2005
This letter proposes a simple, scalable, practical and systematic rule for tuning the control parameter of the adaptive virtual queue (AVQ) active queue management scheme. An explicit stability condition of AVQ is proposed using classical control theory. Theoretical analyses as well as simulation results are used to validate the result.
null Liansheng Tan, null Yan Yang, null Chuang Lin, null Naixue Xiong, M. Zukerman   +4 more
openaire   +1 more source

Tuning of 802.11e network parameters

IEEE Communications Letters, 2006
This paper introduces a mechanism which dynamically tunes the parameters of the 802.11e contention-based access method.
J. Freitag, N.L.S. da Fonseca, J.F. de Rezende   +2 more
openaire   +1 more source

Parameter tuning for meta-heuristics

Knowledge-Based Systems, 2020
Abstract These days meta-heuristic algorithms are gaining lot of popularity. The performance of the meta-heuristics depends upon the suitable selection of user dependent parameters. Finding the most suitable values for the parameters (fine tuning) is a challenging problem.
Susheel Kumar Joshi, Jagdish Chand Bansal   +1 more
openaire   +1 more source

Design weighting parameter tuning of multivariable self-tuning controllers

Computers & Electrical Engineering, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cho, Won Chul, Lee, In Soo, Kim, Kyung Youn   +2 more
openaire   +2 more sources

Parameter fine-tuning for robots

IEEE Control Systems Magazine, 1989
Fine-tuning the parameters of robots is a time-consuming and costly procedure, so that many commercial robot companies cannot afford to do it and the physical capability of robots is not fully realized. Autotune, a computer procedure that automatically tunes parameters of feedback compensators for robot arms in order to circumvent manual tuning and ...
openaire   +1 more source

Tuning Parameters in Engineering Design

Journal of Mechanical Design, 1991
In the design and manufacture of mechanical devices, there are parameters whose values are determined by the manufacturing process in response to errors introduced in the devices’s manufacture or operating environment. Such parameters are termed tuning parameters, and are distinct from design parameters which the designer selects values for as a part ...
Kevin N. Otto, Erik K. Antonsson
openaire   +1 more source

Parameter Tuning with User Models

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014
Can we effectively influence aggregate user behavior in a cluster based retrieval (CBR) system by tuning its parameters? This question combines parameter tuning with models of user behavior. To address this question, we propose an approach based on three components: user model, criterion metric, and sensitivity analysis.
openaire   +1 more source

Tuning the Parameters: A Maritime-Tuned Machine Learning Course

2023 ASEE Annual Conference & Exposition Proceedings
In machine learning (ML) education, the choice of which datasets to utilize for student assignments and projects is critical for student success and meeting course learning outcomes. Poorly chosen datasets leave students disinterested and questioning the applicability of ML in real-world situations specific to their intended endeavors post academia ...
Vincenzo Antonio Ventricelli, Paul M. Kump, Van-Hai Bui   +2 more
openaire   +2 more sources