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| ODE Neural× | LSTM× | Jaringan Saraf Berulang (Recurrent Neural Network - RNN)× | |
|---|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 2018 | 1997 | 1986–1990 |
| Pencetus≠ | Chen, T. Q. et al. | Hochreiter, S. & Schmidhuber, J. | Rumelhart, D. E.; Elman, J. L. |
| Tipe≠ | Continuous-depth neural network (ODE-parameterised dynamics) | Recurrent neural network (gated memory cell) | Sequential neural network |
| Sumber perintis≠ | Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS). link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Alias | 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 | RNN, Elman network, Jordan network, simple recurrent network |
| Terkait≠ | 4 | 5 | 3 |
| Ringkasan≠ | 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. | 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. |
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