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1 Ergebnisse
1
An Empirical Study of Parameter-Efficient Fine-Tuning Metho..:
, In:
2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
,
Liu, Jiaxing
;
Sha, Chaofeng
;
Peng, Xin
- p. 397-408 , 2023
Link:
https://doi.org/10.1109/ASE56229.2023.00125
RT T1
2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
: T1
An Empirical Study of Parameter-Efficient Fine-Tuning Methods for Pre-Trained Code Models
UL https://suche.suub.uni-bremen.de/peid=ieee-10298587&Exemplar=1&LAN=DE A1 Liu, Jiaxing A1 Sha, Chaofeng A1 Peng, Xin YR 2023 SN 2643-1572 K1 Training K1 Adaptation models K1 Codes K1 Costs K1 Computational modeling K1 Transfer learning K1 Memory management K1 pre-trained code models K1 fine-tuning K1 parameter-efficient K1 transfer learning SP 397 OP 408 LK http://dx.doi.org/https://doi.org/10.1109/ASE56229.2023.00125 DO https://doi.org/10.1109/ASE56229.2023.00125 SF ELIB - SuUB Bremen
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