Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Згорнута згорткова мережа із розширенням× | Блокований рекурентний блок (GRU)× | Модель послідовність-послідовність× | |
|---|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning | Machine learning |
| Рік появи≠ | 2016 | 2014 | 2014 |
| Автор методу≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Cho, K. et al. | Sutskever, I.; Cho, K. |
| Тип≠ | Deep learning (dilated 1D convolutional network) | Gated recurrent neural network unit | Encoder-decoder neural network (deep learning) |
| Основоположне джерело≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | 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 ↗ |
| Інші назви≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network | Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning |
| Пов'язані | 5 | 5 | 5 |
| Підсумок≠ | A Dilated CNN is a one-dimensional convolutional network whose receptive field grows exponentially with depth, letting it model long-range structure in time series and audio signals. WaveNet (van den Oord et al., 2016) and the Temporal Convolutional Network of Bai, Kolter and Koltun (2018) are the prominent members of this family. | 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. |
| ScholarGateНабір даних ↗ |
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