手法を比較

	ニューラルODE	リカレントニューラルネットワーク (RNN)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2018	1986–1990
提唱者	Chen, T. Q. et al.	Rumelhart, D. E.; Elman, J. L.
種類	Continuous-depth neural network (ODE-parameterised dynamics)	Sequential neural network
原典	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.
別名	Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net	RNN, Elman network, Jordan network, simple recurrent network
関連	4	3
概要	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.