Comparar métodos

	Destilación de Conocimiento	Búsqueda de Arquitecturas Neuronales	Random Forest
Campo	Aprendizaje profundo	Aprendizaje profundo	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2015	2017	2001
Autor original	Hinton, G., Vinyals, O. & Dean, J.	Zoph, B. & Le, Q.V.	Breiman, L.
Tipo	Neural network compression (teacher–student)	Automated architecture optimization (deep learning)	Ensemble (bagging of decision trees)
Fuente seminal	Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	5	5	4
Resumen	Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster.	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	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.