What are the drawbacks of Recurrent Neural Networks (RNNs)?

I RNNs can only solve regression problems.
II RNNs can only produce single-valued outputs.
III RNNs suffer from vanishing gradients, which make it difficult to know which direction the parameters should move, and exploding gradients, which can make learning unstable.
IV One can only use the sigmoid function as the activation function for its hidden layers.

Question

What are the drawbacks of Recurrent Neural Networks (RNNs)?

I	RNNs can only solve regression problems.
II	RNNs can only produce single-valued outputs.
III	RNNs suffer from vanishing gradients, which make it difficult to know which direction the parameters should move, and exploding gradients, which can make learning unstable.
IV	One can only use the sigmoid function as the activation function for its hidden layers.

BlackTom AI · Accepted Answer

The question asks about the drawbacks of Recurrent Neural Networks (RNNs) and presents four statements for evaluation.
Option I: 'RNNs can only solve regression problems.' This is incorrect. RNNs are designed for sequential data and can be used for a variety of tasks, including sequence classification, sequence labeling, and forecasting, not limited to regression.
Option II: 'RNNs can only produce single-valued outputs.' This is not true. RNNs can produce sequences or multiple outputs over time (many-to-one, one-to-many, or many-to-many architectures), so producing more than a single value is entirely feasible.
Option III: 'RNNs suffer from vanishing gradients, which make it difficult to know which direction the parameters should move, and exploding gradients, which can make learning unstable.' This statement accurately captures well-known training difficulties with vanilla RNNs. The vanishing gradient problem hampers learning long-range dependencies, while exploding gradients can cause numeric instability during backpropagation through time.
Option IV: 'One can only use the sigmoid function as the activation function for its hidden layers.' This is incorrect. Although the sigmoid function has been used historically, RNNs commonly employ other activations such as tanh, and in some cases ReLU variants, depending on the architecture and application. The claim that only sigmoid is allowed is false.
In summary, three of the statements mischaracterize RNN capabilities (I, II, IV), while the third accurately identifies a core drawback of vanilla RNNs (III).

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