Machine Learning · 2026-10-28

Exponential Wake-Up Overshoot in Explicit Gaussian Point-Process Filters

arXiv:2610.35958v1 Announce Type: new Abstract: When events resume after a long silence, explicit Gaussian point-process filters amplify the first update exponentially in the silence duration. We give the exact distribution of this update and a computable index that predicts when it overshoots.

SHARE THIS ARTICLE LinkedIn X Facebook

arXiv:2610.35958v1 Announce Type: new Abstract: When events resume after a long silence, explicit Gaussian point-process filters amplify the first update exponentially in the silence duration. We give the exact distribution of this update and a computable index that predicts when it overshoots. For the explicit (single-scoring-step) filter and its implicit posterior-mode counterpart, we derive second-order silence expansions of the state variance with opposite-sign logarithmic corrections. The crossing index determines when the post-wake prediction exceeds the resumed rate, separating the bin width at which the amplification exponent reaches one from the larger width at which the true rate is overshot.

Source and attribution

Original source: arXiv ML ↗

This is an attributed editorial digest. For the complete study and original reporting, follow the source link.

Reader feedback

0 likes · 0 dislikes

Sign in to like or dislike this research.

SHARE THIS ARTICLE LinkedIn X Facebook
← Back to research archive