Unverified50% confidenceFactExact time
Excessively large weights may cause the Sigmoid activation function to saturate, triggering vanishing gradients.
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50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
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Related Claims
VerifiedThe vanishing gradient problem originates from saturating activation functions such as Sigmoid or Tanh, whose derivatives approach zero for large or small input values, causing gradients to decay layer by layer.72% similarUnverifiedHigh UTD exacerbates the deadly triad problem (instability arising from the coexistence of function approximation, bootstrapping, and off-policy learning), which can lead to Q-value divergence or value function collapse.68% similarUnverifiedsigmoid激活函数的导数最大值仅为0.25，多层连乘后导致梯度迅速消失67% similarVerifiedRNNs suffer from vanishing/exploding gradient problems, where gradients propagating across a large number of time steps decay or grow exponentially, making it difficult to learn long-range dependencies.64% similarUnverifiedRepeatedly applying the same set of weights can easily cause gradients to explode or vanish across multiple iterations, similar to the gradient problems of early RNNs.64% similar
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