Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Învățare prin transfer cu LSTM× | Long Short-Term Memory (LSTM)× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2018 (ULMFiT; concept since ~2010) | 1997 |
| Autorul original≠ | Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010) | Hochreiter, S. & Schmidhuber, J. |
| Tip≠ | Transfer learning / Sequential model | Recurrent neural network with gated memory cells |
| Sursa seminală≠ | Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339. DOI ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| Denumiri alternative | LSTM Transfer Learning, Pre-trained LSTM, LSTM Fine-Tuning, ULMFiT-style LSTM Transfer | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce. | Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step. |
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