稳健性(进化)
人工神经网络
水准点(测量)
计算机科学
人工智能
预测区间
机器学习
回归
深度学习
深层神经网络
预测建模
钥匙(锁)
数据挖掘
数学
统计
生物化学
基因
计算机安全
化学
地理
大地测量学
作者
Eli Simhayev,Gilad Katz,Lior Rokach
出处
期刊:Cornell University - arXiv
日期:2021-05-04
被引量:2
摘要
Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We present PIVEN, a deep neural network for producing both a PI and a prediction of specific values. Unlike previous studies, PIVEN makes no assumptions regarding data distribution inside the PI, making its point prediction more effective for various real-world problems. Benchmark experiments show that our approach produces tighter uncertainty bounds than the current state-of-the-art approach for producing PIs, while maintaining comparable performance to the state-of-the-art approach for specific value-prediction. Additional evaluation on large image datasets further support our conclusions.
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