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| ゲート付き再帰ユニット (GRU)× | シーケンス・ツー・シーケンスモデル× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年 | 2014 | 2014 |
| 提唱者≠ | Cho, K. et al. | Sutskever, I.; Cho, K. |
| 種類≠ | Gated recurrent neural network unit | Encoder-decoder neural network (deep learning) |
| 原典≠ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ | Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗ |
| 別名≠ | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning |
| 関連 | 5 | 5 |
| 概要≠ | 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 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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