ScholarGate
Asistent

Porovnat metody

Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Dilated CNN×Obousměrná rekurentní neuronová síť×Model sekvence-sekvence×
OborHluboké učeníHluboké učeníHluboké učení
RodinaMachine learningMachine learningMachine learning
Rok vzniku201619972014
Tvůrcevan den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.Schuster, M. & Paliwal, K.K.Sutskever, I.; Cho, K.
TypDeep learning (dilated 1D convolutional network)Recurrent neural network (sequence model)Encoder-decoder neural network (deep learning)
Původní zdrojvan 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 ↗Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS. link ↗
Další názvyDilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCNÇift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRUDizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Příbuzné555
ShrnutíA 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 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.
ScholarGateDatová sada
  1. v1
  2. 2 Zdroje
  3. PUBLISHED
  1. v1
  2. 2 Zdroje
  3. PUBLISHED
  1. v1
  2. 2 Zdroje
  3. PUBLISHED

Přejít na hledání Stáhnout prezentaci

ScholarGatePorovnat metody: Dilated CNN · Bidirectional RNN · Sequence-to-Sequence Model. Získáno 2026-06-18 z https://scholargate.app/cs/compare