MLF-core：一个用于确定性机器学习的框架
mlf-core: a framework for deterministic machine learning
摘要：
近年来，机器学习显示出广泛的增长，现在经常应用于敏感领域。为了允许在部署之前对预测模型进行适当的验证，模型必须是确定性的。然而，主要的机器学习库默认使用基于原子操作的非确定性算法。仅仅固定所有随机种子对于确定性机器学习是不够的。为了克服这一缺点，各种机器学习库发布了确定性算法的非确定性算法。我们评估了这些算法对确定性和运行时间的影响。基于这些结果，我们制定了一套确定性机器学习的要求，并开发了一个新的软件解决方案，mlf核心生态系统，它帮助机器学习项目满足并保持这些要求。我们应用mlf-core开发了各种生物医学领域的确定性模型，包括使用TensorFlow的单细胞自动编码器，用于CT扫描中肝脏肿瘤分割的基于PyTorch的U-Net模型，以及基于XGBoost的基因表达谱的肝癌分类器。
英文摘要：
Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas. To allow appropriate verification of predictive models before deployment, models must be deterministic. However, major machine learning libraries default to the usage of non-deterministic algorithms based on atomic operations. Solely fixing all random seeds is not sufficient for deterministic machine learning. To overcome this shortcoming, various machine learning libraries released deterministic counterparts to the non-deterministic algorithms. We evaluated the effect of these algorithms on determinism and runtime. Based on these results, we formulated a set of requirements for deterministic machine learning and developed a new software solution, the mlf-core ecosystem, which aids machine learning projects to meet and keep these requirements. We applied mlf-core to develop deterministic models in various biomedical fields including a single cell autoencoder with TensorFlow, a PyTorch-based U-Net model for liver-tumor segmentation in CT scans, and a liver cancer classifier based on gene expression profiles with XGBoost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
