방법 비교

	LoRA 및 PEFT	랜덤 포레스트	Variational Autoencoder
분야	딥러닝	머신러닝	딥러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2022	2001	2014
창시자	Hu, E. J. et al.; Lester, B. et al.	Breiman, L.	Kingma, D. P. & Welling, M.
유형	Parameter-efficient fine-tuning of large pretrained models	Ensemble (bagging of decision trees)	Deep generative latent-variable model (encoder–decoder)
원전	Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
별칭	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	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
관련	5	4	5
요약	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.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.