Vanilla RNN Implementation and Optimization

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

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)

Input and output gates

Memory cells and hidden states

Weight matrices and bias vectors (correct)

Predictors and information retainers

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

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

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

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

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