Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| CNN Dilatada× | RNN Bidirecional× | Unidade Recorrente Gated (GRU)× | |
|---|---|---|---|
| Área | Aprendizado profundo | Aprendizado profundo | Aprendizado profundo |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 2016 | 1997 | 2014 |
| Autor original≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Schuster, M. & Paliwal, K.K. | Cho, K. et al. |
| Tipo≠ | Deep learning (dilated 1D convolutional network) | Recurrent neural network (sequence model) | Gated recurrent neural network unit |
| Fonte seminal≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ | Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP. link ↗ |
| Outros nomes≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU | Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network |
| Relacionados | 5 | 5 | 5 |
| Resumo≠ | 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. | A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition. | 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. |
| ScholarGateConjunto de dados ↗ |
|
|
|