A researcher modifies the position-wise sub-layer within a sequence processing model. The standard design for this sub-layer is a sequence of: a linear transformation, a non-linear activation, and a second linear transformation. The researcher's modification adds a second non-linear activation function immediately after the final linear transformation. Which of the following best evaluates the impact of this architectural change?
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Feed-Forward Network (FFN) Formula and Component Dimensions in Transformers
An engineer is building a deep neural network for sequence processing. Each layer of the network consists of a self-attention mechanism followed by a position-wise sub-layer. The engineer designs this position-wise sub-layer to be composed of two consecutive linear transformations. What is the most significant negative consequence of omitting a non-linear activation function between these two linear transformations?
Analysis of a Position-Wise Sub-Layer
A researcher modifies the position-wise sub-layer within a sequence processing model. The standard design for this sub-layer is a sequence of: a linear transformation, a non-linear activation, and a second linear transformation. The researcher's modification adds a second non-linear activation function immediately after the final linear transformation. Which of the following best evaluates the impact of this architectural change?
FFN Hidden Size in Transformers