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| Aluekohtaisesti mukautuva toistuva hermoverkko× | Rekurrentti neuroverkko× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2010s | 1986–1990 |
| Kehittäjä≠ | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) | Rumelhart, D. E.; Elman, J. L. |
| Tyyppi≠ | Domain-adaptive sequential model | Sequential neural network |
| Alkuperäislähde≠ | Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Rinnakkaisnimet | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN | RNN, Elman network, Jordan network, simple recurrent network |
| Liittyvät≠ | 6 | 3 |
| Tiivistelmä≠ | A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable. | 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. |
| ScholarGateAineisto ↗ |
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