Unverified50% confidenceFactExact time
When neural operators are deployed autoregressively, prediction errors are distributed extremely unevenly in space and time, while the number of local corrections available is very limited.
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAn intermittently faulty memory module may alternately produce correct and incorrect computation results, causing sporadic deviations in gradient calculations during neural network training, affecting model convergence without triggering hardware alerts.77% similarUnverifiedMeasurement error not only shrinks regression coefficients but also undermines the ability to identify the shape of the function.74% similarUnverified近似遗忘通过梯度上升等方法主动削弱特定数据对模型参数的影响，但效果难以量化验证71% similarUnverifiedThe deconvolution problem can be formulated as a division operation in the frequency domain, and its difficulty depends on the decay rate of the characteristic function of the error distribution71% similarUnverified绝对偏差通常可通过标定来补偿，而重复性差意味着误差随机不可预测，很难通过校准解决71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/983239API
curl https://kongchang.com/api/v1/knowledge/claims/983239MCP
get_claim(id=983239)