Unverified50% confidenceFactExact time
Automatic differentiation dynamically constructs a directed acyclic graph (computation graph) by recording each operation and its inputs during every forward computation, and during backpropagation it traverses the graph's reverse edges, sequentially invoking each operation's local gradient function to accumulate gradients via the chain rule
1
Sources
50%
Confidence
Long-term
Relevance
9/21/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedWhen loss.backward() is called, PyTorch traverses the computation graph in reverse, using the chain rule to automatically calculate the gradient of each parameter.76% similarUnverifiedIn LangGraph, each node represents a computation step (such as an LLM call, tool execution, or conditional judgment), and edges represent state transition paths.70% similarUnverifiedStride records the number of memory units that must be skipped to move one step in each dimension, enabling operations such as transposition and slicing to be completed by modifying strides without copying data69% similarUnverifiedIn automatic differentiation, when a variable affects the loss function through multiple computation paths, the gradients from each path must be summed (gradient accumulation).69% similarVerifiedLangGraph通过内置的Checkpointer机制支持将图执行的每一步状态快照持久化，实现断点续跑能力69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/939631API
curl https://kongchang.com/api/v1/knowledge/claims/939631MCP
get_claim(id=939631)