arXiv 2610.26802 · cs.LG · 2026-10-19
Scaling Parameter-Efficient Fine-Tuning for Brain Encoding
Zhuofan Josh Ying, Guanglong Sun, Nolan Ho, Kieran Liu, Rui Yang, Andrew Chen, Nikolaus Kriegeskorte, Pony Ma 等 9 人
Mind Lab
在最新榜 #129 2026-W43 第 7 名(进行中) 本站收录于 2026-10-21
一句话要点
用参数高效微调把最大 104B 的多模态模型适配到脑编码任务,更准确地预测大脑对刺激的反应。
Uses parameter-efficient fine-tuning to adapt multimodal backbones of up to 104B for brain encoding, better predicting neural responses.
阅读 arXiv 原文 PDF Hugging Face 讨论
主题与学科
同主题:AI for Science
- EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery ▲112
- Predictive Credit: Measuring What Scientific Explanations Add to Experimental Forecasts ▲103
- PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs ▲65
- From Research Goals to Scientific Closure: Evaluating Agents Through Evidence-Supported Physics Research ▲53
- Does Learning Protein Folding Generalize to Broader Reasoning? ▲51
数据说明
元数据来自 arXiv API(已核实);点赞来自 Hugging Face 每日论文;引用来自 Semantic Scholar。最近刷新 2026-10-22。
要点由本站根据摘要自写,不转载摘要原文,请阅读原文。