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Questions and Answers
What happens when the entries of the output gate in LSTM approach 1?
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?
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?
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?
How do bidirectional RNNs differ from traditional RNNs?
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What does an output gate close to 0 indicate in LSTM?
What does an output gate close to 0 indicate in LSTM?
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For what type of systems are RNNs primarily designed?
For what type of systems are RNNs primarily designed?
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How do bidirectional RNNs leverage past and future observations?
How do bidirectional RNNs leverage past and future observations?
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What differentiates bidirectional RNNs from traditional RNNs in terms of operation?
What differentiates bidirectional RNNs from traditional RNNs in terms of operation?
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