Unverified50% confidenceFactExact time
雅可比矩阵的计算是反向传播算法的数学基础，捕获完整的一阶线性近似关系，能刻画输入空间任意方向扰动对输出的影响
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Jacobian-Lens in Practice: Manually Reshaping LLM Behavior with J-Space
redditr/LocalLLaMA7/11/2026
Related Claims
VerifiedThe mathematical foundation of the backpropagation algorithm is the chain rule in calculus, which decomposes the gradient of a composite function into the product of local gradients at each layer, propagating the error signal layer by layer from the output layer to the input layer.82% similarVerified反向传播算法的本质是通过链式法则在计算图上高效计算标量损失对每个参数的偏导数76% similarVerifiedBackpropagation is essentially a dynamic programming algorithm for efficiently computing gradients, which reduces the computational complexity of gradient calculation by propagating error signals layer by layer from the output layer to the input layer and reusing intermediate computation results.75% similarVerified神经网络的反向传播算法是链式法则的直接应用，梯度下降依赖微积分中的偏导数概念74% similarUnverifiedAutomatic differentiation differs from symbolic differentiation and numerical differentiation in that it uses the chain rule to decompose the derivative of a composite function into a combination of derivatives of elementary operations, and it is divided into forward mode and reverse mode.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/501496API
curl https://kongchang.com/api/v1/knowledge/claims/501496MCP
get_claim(id=501496)