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	Splotowa sieć konwolucyjna z rozszerzeniem (Dilated CNN)	Dwukierunkowa sieć rekurencyjna	Random Forest	Model sekwencyjny do sekwencyjnego (Seq2Seq)
Dziedzina	Uczenie głębokie	Uczenie głębokie	Uczenie maszynowe	Uczenie głębokie
Rodzina	Machine learning	Machine learning	Machine learning	Machine learning
Rok powstania	2016	1997	2001	2014
Twórca	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Schuster, M. & Paliwal, K.K.	Breiman, L.	Sutskever, I.; Cho, K.
Typ	Deep learning (dilated 1D convolutional network)	Recurrent neural network (sequence model)	Ensemble (bagging of decision trees)	Encoder-decoder neural network (deep learning)
Źródło pierwotne	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
Inne nazwy	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Pokrewne	5	5	4	5
Podsumowanie	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.