Unverified50% confidenceFactExact time
The 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)
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGPU并行归约操作的浮点累加顺序不固定导致传统Transformer注意力机制的实际输出存在微小的不可复现波动64% similarUnverifiedRWKV通过线性注意力加时间衰减的WKV算子实现线性递归架构效果64% similarUnverifiedBilinear interpolation is differentiable with respect to offsets, allowing gradients of sampling positions to be propagated through backpropagation, which is the mathematical foundation for end-to-end training of deformable attention.64% similarUnverified等变神经网络的等变性使输出随输入的旋转或平移变换而以相同方式变换，这种归纳偏置提高了样本效率64% similarUnverifiedTransformer的自注意力机制通过Query-Key-Value矩阵运算决定每个token关注哪些其他token，注意力权重是总和为1的概率分布63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/904257API
curl https://kongchang.com/api/v1/knowledge/claims/904257MCP
get_claim(id=904257)