Slice-based Learning：关键数据片上剩余学习的编程模型
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
摘要：
在现实世界的机器学习应用中，数据子集对应于特别关键的结果：脆弱的骑自行车者检测在自动驾驶任务中是安全关键的，并且“问题”句子可能对对话代理的语言理解很重要。虽然机器学习模型可以在粗粒度指标（如F1分数和整体准确性）上实现高质量的性能，但它们在关键子集上可能表现不佳-我们将这些定义为切片，这是我们方法中的关键抽象。为了解决切片级性能，从业者通常在切片子集上训练单独的“专家”模型或使用多任务硬参数共享。我们提出了基于切片的学习，一个新的编程模型，其中切片功能（SF），编程接口，指定关键数据子集的模型应该提交额外的容量。任何模型都可以利用SF来学习切片专家表示，切片专家表示与注意力机制相结合，以进行切片感知预测。我们表明，我们的方法保持了参数有效的表示，同时在切片上提高了19.0 F1，在跨越语言理解（例如SuperGLUE），计算机视觉和生产规模工业系统的数据集上提高了4.6 F1。
英文摘要：
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes. While machine learning models can achieve high quality performance on coarse-grained metrics like F1-score and overall accuracy, they may underperform on critical subsets---we define these as slices, the key abstraction in our approach. To address slice-level performance, practitioners often train separate "expert" models on slice subsets or use multi-task hard parameter sharing. We propose Slice-based Learning, a new programming model in which the slicing function (SF), a programming interface, specifies critical data subsets for which the model should commit additional capacity. Any model can leverage SFs to learn slice expert representations, which are combined with an attention mechanism to make slice-aware predictions. We show that our approach maintains a parameter-efficient representation while improving over baselines by up to 19.0 F1 on slices and 4.6 F1 overall on datasets spanning language understanding (e.g. SuperGLUE), computer vision, and production-scale industrial systems.
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。
