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Not so binary or generalizable: Brain sex differences with artificial neural networks.

作者信息

Lockhart Jeffrey W, Fuentes Agustín, Rippon Gina, Eliot Lise

机构信息

Department of Sociology, University of Chicago, Chicago, IL 60637.

Department of Anthropology, Princeton University, Princeton, NJ 08544.

出版信息

Proc Natl Acad Sci U S A. 2025 Jan 14;122(2):e2411917121. doi: 10.1073/pnas.2411917121. Epub 2025 Jan 2.

DOI:10.1073/pnas.2411917121
PMID:39746005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11745344/
Abstract
摘要

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Hum Brain Mapp. 2024 Apr;45(5):e26671. doi: 10.1002/hbm.26671.
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Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization.深度学习模型揭示了人类功能大脑组织中可复制、可推广且与行为相关的性别差异。
Proc Natl Acad Sci U S A. 2024 Feb 27;121(9):e2310012121. doi: 10.1073/pnas.2310012121. Epub 2024 Feb 20.
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Because the machine can discriminate: How machine learning serves and transforms biological explanations of human difference.因为机器能够辨别:机器学习如何服务并转变关于人类差异的生物学解释。
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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.停止为高风险决策解释黑箱机器学习模型,转而使用可解释模型。
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