Comparar métodos

	CNN Dilatada	Unidade Recorrente Gated (GRU)
Área	Aprendizado profundo	Aprendizado profundo
Família	Machine learning	Machine learning
Ano de origem	2016	2014
Autor original	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Cho, K. et al.
Tipo	Deep learning (dilated 1D convolutional network)	Gated recurrent neural network unit
Fonte seminal	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.
Outros nomes	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network
Relacionados	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.	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.