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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| LoRA e PEFT× | Rete Generativa Avversaria× | Random Forest× | |
|---|---|---|---|
| Campo≠ | Apprendimento profondo | Apprendimento profondo | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning | Machine learning |
| Anno di origine≠ | 2022 | 2014 | 2001 |
| Ideatore≠ | Hu, E. J. et al.; Lester, B. et al. | Goodfellow, I. et al. | Breiman, L. |
| Tipo≠ | Parameter-efficient fine-tuning of large pretrained models | Generative deep learning (adversarial two-network game) | Ensemble (bagging of decision trees) |
| Fonte seminale≠ | Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias≠ | LoRA ve PEFT — Parametre Verimli İnce Ayar, Low-Rank Adaptation, parameter-efficient fine-tuning, prefix tuning | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Correlati≠ | 5 | 4 | 4 |
| Sintesi≠ | 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. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. | 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. |
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