arXiv cs.AI· Yunni Qu (Department of Computer Science, University of North Carolina at Chapel Hill), Bing Cai Kok (Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, School of Social Sciences, Nanyang Technological University, Singapore), Whitney Ringwald (Department of Psychology, University of Minnesota Twin Cities), Grant King (Department of Psychology, University of Michigan), Aidan Wright (Department of Psychology, University of Michigan), Kathleen Gates (Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill), Junier Oliva (Department of Computer Science, University of North Carolina at Chapel Hill)·· 13 小时前AI 评分17
用树蒸馏为纵向主动特征获取学习可解释策略，降低参与者负担
Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden
AI 导读
针对纵向研究中预测精度与参与者负担的矛盾，研究者提出一种树蒸馏方法，从基于神经网络的 Longitudinal Active Feature Acquisition（LAFA）网络中学习可解释策略。该方法在模拟实验和预测每日饮酒量的 EMA 实证数据集上验证，均能在精度损失极小的前提下显著减少每次采集的条目数量。
来源：arXiv cs.AI · arxiv.org