Сравнение на методи
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| LoRA и PEFT× | Случайна гора× | |
|---|---|---|
| Област≠ | Дълбоко обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2022 | 2001 |
| Създател≠ | Hu, E. J. et al.; Lester, B. et al. | Breiman, L. |
| Тип≠ | Parameter-efficient fine-tuning of large pretrained models | Ensemble (bagging of decision trees) |
| Основополагащ източник≠ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Други названия≠ | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Свързани≠ | 5 | 4 |
| Резюме≠ | LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateНабор от данни ↗ |
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