Choose ALL correct statements about RNN. Scoring: Right Minus Wrong a) One can use RNN to do Vec2Seq by taking the input observed at time point 1 and predict output ( y ) at each time point auto-regressively. b) In all Vec2Seq, Seq2Seq, Seq2Vec applications, we can stack multiple LSTM or GRU layers to extract deep representation of the data. c)

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Choose ALL correct statements about RNN. Scoring: Right Minus Wrong a) One can use RNN to do Vec2Seq by taking the input observed at time point 1 and predict output  ( y  ) at each time point auto-regressively. b) In all Vec2Seq, Seq2Seq, Seq2Vec applications, we can stack multiple LSTM or GRU layers to extract deep representation of the data. c) Vanishing gradient problem occurs in RNN when we are modelling long sequence of data. d) In the above Seq2Seq unaligned neural machine translation RNN model, the context vector  (  mathrm{c}^{ wedge} {[ }  ) at each hidden layer is only used when computing the hidden state at time point 1 in the decoder.

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