Vanilla RNN Implementation and Optimization
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Vanilla RNN Implementation and Optimization

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Questions and Answers

What happens when the entries of the output gate in LSTM approach 1?

  • The hidden state variables are jointly mapped into the inferred label
  • All memory information is passed through to the predictor (correct)
  • No further processing is done and information is retained within the memory cell
  • The model resets all memory cells

    • In what scenarios is bidirectional RNN useful?

  • When future observations are crucial for inferring a label
  • When only a single RNN is needed
  • When only past observations are required for labeling
  • For signal smoothing and denoising scenarios (correct)

    • What are the trainable parameters in LSTM networks?

  • Input and output gates
  • Memory cells and hidden states
  • Weight matrices and bias vectors (correct)
  • Predictors and information retainers

    • How do bidirectional RNNs differ from traditional RNNs?

    <p>Bidirectional RNNs combine causal and anti-causal operations</p> Signup and view all the answers

    What does an output gate close to 0 indicate in LSTM?

    <p>No further processing is done and information is retained within the memory cell</p> Signup and view all the answers

    For what type of systems are RNNs primarily designed?

    <p>Causal systems with inference based on observed inputs until a given time instance</p> Signup and view all the answers

    How do bidirectional RNNs leverage past and future observations?

    <p>By combining past and future operations through distinct RNNs</p> Signup and view all the answers

    What differentiates bidirectional RNNs from traditional RNNs in terms of operation?

    <p>Bidirectional RNNs combine causal and anti-causal operations</p> Signup and view all the answers

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