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شبكة الالتفاف المتمددة (Dilated CNN)×وحدة التكرار المسورة (GRU)×نموذج التسلسل إلى التسلسل×
المجالالتعلم العميقالتعلم العميقالتعلم العميق
العائلةMachine learningMachine learningMachine learning
سنة النشأة201620142014
صاحب الطريقةvan den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.Cho, K. et al.Sutskever, I.; Cho, K.
النوعDeep learning (dilated 1D convolutional network)Gated recurrent neural network unitEncoder-decoder neural network (deep learning)
المصدر التأسيسيvan 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 ↗
الأسماء البديلةDilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCNKapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent networkDizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
ذات صلة555
الملخصA 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.
ScholarGateمجموعة البيانات
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ScholarGateقارن الطرق: Dilated CNN · GRU · Sequence-to-Sequence Model. استُرجع بتاريخ 2026-06-18 من https://scholargate.app/ar/compare