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Related Questions
- How do RNNs' vanishing/exploding gradients affect the learning of long-term dependencies?
- What type of RNN architectures help mitigate the vanishing/explosions gradients issue for long-term dependencies?
- How do gated architectures, such as LSTMs, handle long-term dependencies compared to standard RNNs?
- What is the tradeoff between increased model capacity and the difficulty of training such models when handling long-term dependencies?
- Do RNNs' recurrence and stateful design impede or facilitate the convergence of training for long-range dependencies?
- For what type of sequential datasets are RNNs insufficient for modeling long-term patterns, and what are other alternatives?
- Given the fundamental limitations of traditional RNNs, are recent advancements in transformer models resolving the challenges of handling long-term dependencies?
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