Порівняння методів

	LoRA та PEFT	Випадковий ліс	Варіаційний автокодувальник
Галузь	Глибоке навчання	Машинне навчання	Глибоке навчання
Родина	Machine learning	Machine learning	Machine learning
Рік появи	2022	2001	2014
Автор методу	Hu, E. J. et al.; Lester, B. et al.	Breiman, L.	Kingma, D. P. & Welling, M.
Тип	Parameter-efficient fine-tuning of large pretrained models	Ensemble (bagging of decision trees)	Deep generative latent-variable model (encoder–decoder)
Основоположне джерело	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).
Інші назви	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
Пов'язані	5	4	5
Підсумок	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.