In a standard Transformer Feed-Forward Network (FFN), the first fully connected layer typically utilizes a non-linear activation function such as ReLU.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.1 Transformer Architecture and Components - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Position-Wise Feed-Forward Networks - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
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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?
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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In a standard Transformer Feed-Forward Network (FFN), the first fully connected layer typically utilizes a non-linear activation function such as ReLU.
Explain the primary purpose of the Feed-Forward Network (FFN) sub-layer in Transformer models, detailing how its function affects representation learning and supports the self-attention mechanism.
In the standard architecture of a Transformer Feed-Forward Network (FFN), what specific type of layer is employed as the second fully connected layer?