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Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text

Paper recorded by Signals 4 on 2026-09-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-30 on arXiv · recorded by Signals 4 on 2026-10-01

Category: cs.LG · 机器学习 · first seen 2026-10-01

Abstract

We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the influential work of d'Ascoli et al. (2025), time series of brain activity from subjects perceiving continuous speech are segmented into fixed-length windows starting at each word. A neural network then generates predictions for all of the words in a

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#15 most recent of 362 cs.LG papers we have recorded · ↑ newer: Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis · ↓ older: Image Classifiers are Efficient Self-Supervised Video Representation L
Cite this page: Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text: the #15 most recent of 362 cs.LG papers we have recorded (as of 2026-09-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/removing-timing-shortcuts-improves-non-invasive-brain-to-text.html
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