In the context of RNNs, what problem does the introduction of gating mechanisms in LSTMs and GRUs aim to address?
- Vanishing and Exploding Gradients
- Overfitting and Data Loss
- Dimensionality Reduction and Compression
- Sequence Length Reduction and Truncation
The introduction of gating mechanisms in LSTMs and GRUs aims to address the problem of vanishing and exploding gradients, which occur during training due to the backpropagation of errors over long sequences. These mechanisms help RNNs capture long-range dependencies in data.
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