Unverified50% confidenceFactExact time
Low-rank parameterization decomposes the covariance matrix into a low-rank factor plus a diagonal matrix form, reducing the parameter count from O(d²) to O(dr).
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA domain decomposition strategy can reduce the step complexity from O((L/w)²) to O(L²/(N·w²)), where N is the number of subdomains.72% similarUnverifiedLow-rank decomposition and quantization compression are orthogonally complementary dimensions; quantizing the smaller matrices A and B separately results in smaller errors, and the combined compression ratio can reach several times that of either method alone70% similarVerifiedLoRA decomposes the weight update matrix ΔW into the product of two small matrices A and B whose ranks are much lower than the original matrix, reducing parameter count to as little as one ten-thousandth of the original or even less.70% similarUnverified直接在SO(3)上做卷积的朴素复杂度为O(B^6)，通过快速球面傅里叶变换（基于Driscoll-Healy或McEwen-Wiaux采样定理）可降低至O(B^5)甚至更低67% similarUnverifiedThe standard approach for handling non-square cost matrices is to pad them into square matrices using large dummy cost values.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/893117API
curl https://kongchang.com/api/v1/knowledge/claims/893117MCP
get_claim(id=893117)