Signals 4 · free daily AI digest

MIRA: Mutual Information guided calibration for Reliable Test-Time Adaptation

Paper recorded by Signals 4 on 2026-11-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-01 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.LG · 机器学习 · first seen 2026-11-03

Abstract

Test-time adaptation (TTA) updates a deployed model using only unlabeled target data, but standard entropy-based methods often rely on deterministic confidence and can reinforce overconfident errors under distribution shift. We propose MIRA, Mutual Information guided calibration for Reliable Test-Time Adaptation, a source-free online adaptation method that uses lightweight last-layer Gaussian pert

Read on arXiv →

#29 most recent of 703 cs.LG papers we have recorded · ↑ newer: Can LLM Agents Improve Imperfect Earth System Model Parameterizations? · ↓ older: Seeing Why: Exact Evidence Maps for Quantum and Optical Image Classifi
Cite this page: MIRA: Mutual Information guided calibration for Reliable Test-Time Adaptation: the #29 most recent of 703 cs.LG papers we have recorded (as of 2026-11-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mira-mutual-information-guided-calibration-for-reliable-test-time-adaptation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions