Results 21 to 30 of about 8,464,096 (315)
An automated system for program parameters fine tuning in the cloud [PDF]
Компьютерные исследования и моделирование, 2015 The paper presents a software system aimed at finding best (in some sense) parameters of an algorithm. The system handles both discrete and continuous parameters and employs massive parallelism offered by public clouds.
S. A. Smirnov, A. S. Tarasov
doaj +1 more source
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models [PDF]
arXiv.org, 2023 Transfer learning via fine-tuning pre-trained transformer models has gained significant success in delivering state-of-the-art results across various NLP tasks.
Sara Babakniya +6 more
semanticscholar +1 more source
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning [PDF]
arXiv.org, 2023 We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a generalized prompt module to optimize pre-trained model weights and adjust intermediate ...
Arnav Chavan +4 more
semanticscholar +1 more source
Efficiently Tuned Parameters Are Task Embeddings
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 EMNLP 2022 (main conference)
Zhou, Wangchunshu +2 more
openaire +2 more sources
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning [PDF]
IEEE International Symposium on Software Reliability Engineering, 2023 The automation of code review activities, a long-standing pursuit in software engineering, has been primarily addressed by numerous domain-specific pre-trained models.
Jun Lu +4 more
semanticscholar +1 more source
GEANT4 parameter tuning using Professor
Journal of Instrumentation, 2020 The Geant4 toolkit is used extensively in high energy physics to simulate the passage of particles through matter and to predict effects such as detector efficiencies and smearing. Geant4 uses many underlying models to predict particle interaction kinematics, and uncertainty in these models leads to uncertainty in high energy physics measurements.
Elvira, V. +12 more
openaire +2 more sources
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning [PDF]
IEEE International Conference on Computer Vision, 2023 Diffusion models have proven to be highly effective in generating high-quality images. However, adapting large pre-trained diffusion models to new domains remains an open challenge, which is critical for real-world applications.
Enze Xie +7 more
semanticscholar +1 more source
Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following
Journal of Marine Science and Engineering, 2022 Unmanned surface vessels (USVs) are required to follow a path during a task. This is essential for the USV, especially when following a curvilinear path or considering the interference of waves, and this work has been proven to be complicated.
Lifei Song +4 more
semanticscholar +1 more source
Hierarchical Collaborative Hyper-Parameter Tuning
, 2022 Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model.
Ahmad Esmaeili +2 more
openaire +2 more sources
Research on application of ZigBee technology in PID controller parameter tuning
Gong-kuang zidonghua, 2016 In view of problem of traditional PID parameter tuning such as installation and wiring of cable are quite cumbersome,operation lacks flexibility, the paper proposed to apply ZigBee technology to PID parameter tuning.
LIU Xianfeng +2 more
doaj +1 more source