Σύγκριση μεθόδων

	Νευρωνική Συνήθης Διαφορική Εξίσωση (Neural ODE)	LSTM	Τυχαίο Δάσος
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	2018	1997	2001
Δημιουργός	Chen, T. Q. et al.	Hochreiter, S. & Schmidhuber, J.	Breiman, L.
Τύπος	Continuous-depth neural network (ODE-parameterised dynamics)	Recurrent neural network (gated memory cell)	Ensemble (bagging of decision trees)
Θεμελιώδης πηγή	Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Εναλλακτικές ονομασίες	Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Συναφείς	4	5	4
Σύνοψη	A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physics-based modelling.	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.	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.