Paper

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

Unreadunread

AI summary

arXiv:2606. 24974v1 Announce Type: new Abstract: Explainability techniques are used to assess the output of various deep learning models.

Reading status

Priority

About this paper

Authors: David A. Kelly, Nathan Blake
Released: 2026-06-25 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, rate, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.24974

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.