Unverified50% confidenceFactExact time
In multi-head attention, the outputs of each head are concatenated and then fused through a linear transformation.
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Long-term
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11/2/2026
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UnverifiedMulti-head attention splits query/key/value into multiple heads computed in parallel, each head learning a different attention pattern, and the results are concatenated and linearly transformed.78% similarUnverified将自回归基座改造为扩散模型通常涉及将因果注意力掩码替换为双向注意力，并引入噪声调度器和时间步嵌入71% similarUnverifiedDiffusionGemma依赖双向注意力（去掉因果注意力的下三角掩码），使每个位置能同时关注序列中前后所有位置70% similarUnverifiedThe feed-forward network is located after the attention sublayer and typically contains two linear transformations and a nonlinear activation function (such as ReLU or GeLU)69% similarUnverifiedThe self-attention mechanism linearly transforms the input sequence into three matrices—Query, Key, and Value—with attention weights obtained by multiplying Q by the transpose of K, normalizing via Softmax, and then multiplying by V to produce the output.69% similar
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