Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Gated Recurrent Unit (GRU)× | Tähelepanu mehhanism× | Jadast-jada mudel× | |
|---|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning | Machine learning |
| Tekkeaasta≠ | 2014 | 2015 | 2014 |
| Looja≠ | Cho, K. et al. | Bahdanau, D.; Luong, M.T. | Sutskever, I.; Cho, K. |
| Tüüp≠ | Gated recurrent neural network unit | Neural attention layer (encoder-decoder) | Encoder-decoder neural network (deep learning) |
| Algallikas≠ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ |
| Rööpnimetused≠ | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning |
| Seotud | 5 | 5 | 5 |
| Kokkuvõte≠ | The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters. | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. | The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation. |
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