Usporedite metode

	Neural ODE	Rekurentna neuronska mreža
Područje	Duboko učenje	Duboko učenje
Obitelj	Machine learning	Machine learning
Godina nastanka	2018	1986–1990
Tvorac	Chen, T. Q. et al.	Rumelhart, D. E.; Elman, J. L.
Vrsta	Continuous-depth neural network (ODE-parameterised dynamics)	Sequential neural network
Temeljni izvor	Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Drugi nazivi	Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net	RNN, Elman network, Jordan network, simple recurrent network
Srodne	4	3
Sažetak	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.