同一性能下发现多种不同基因表达模式，揭示模型内在机制差异
Finding Multiple Interpretations in Datasets
- 通过损失/准确率筛选相似性能模型，挖掘其上下文感知特征差异
- 在METABRIC数据集上找到与对照方法完全不同基因表达的模型
- 适合关注模型本质特性的研究人员探索潜在生物学机制
本文提出一种方法，用于发现具有相似性能（以损失/准确率衡量）但上下文感知特征显著不同的模型集合。在METABRIC数据集上的实验表明，该方法找到的模型在基因表达上与对照方法所得结果差异极大，且未造成性能下降。我们主张，当研究者希望分析模型的全局特性以揭示所研究现象的本质时，该方法具有重要意义。
原文摘要 · Abstract (English)
In this paper, we propose an approach to finding sets of similar-performing models (in terms of loss/accuracy measurements) with highly different context-aware characteristics. Through experiments on the METABRIC dataset, we show that the proposed method finds multiple models with highly different gene expressions than those found by the control methodology without performance penalties. We argue that the proposed methodology is important whenever one aims to analyze any global characteristic of a model to extract insight into the underlying phenomenon being studied.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。