论文标题
DeepToppush：简单可扩展的方法，用于顶部的准确性
DeepTopPush: Simple and Scalable Method for Accuracy at the Top
论文作者
论文摘要
顶部的准确性是一类特殊的二进制分类问题，仅在少数相关（顶部）样本上评估性能。应用程序包括通过手动（昂贵）后处理的信息检索系统或过程。这导致最大程度地减少了阈值以上的无关样品的数量。我们考虑以任意（深）网络形式的分类器，并提出了一种新方法，以最大程度地降低顶部的损耗函数。由于阈值取决于所有样本，因此问题是不可分配的。我们修改随机梯度下降，以端到端训练方式处理非分解性，并提出一种方法来估算仅从当前Minibatch上值和一个延迟值的值估算阈值。我们在视觉识别数据集和两个现实世界应用程序上展示了DeepToppush的出色性能。第一个选择少数分子进行进一步的药物测试。第二个使用真实的恶意软件数据，我们以极低的误报率为$ 10^{ - 5} $检测到46 \％恶意软件。
Accuracy at the top is a special class of binary classification problems where the performance is evaluated only on a small number of relevant (top) samples. Applications include information retrieval systems or processes with manual (expensive) postprocessing. This leads to minimizing the number of irrelevant samples above a threshold. We consider classifiers in the form of an arbitrary (deep) network and propose a new method DeepTopPush for minimizing the loss function at the top. Since the threshold depends on all samples, the problem is non-decomposable. We modify the stochastic gradient descent to handle the non-decomposability in an end-to-end training manner and propose a way to estimate the threshold only from values on the current minibatch and one delayed value. We demonstrate the excellent performance of DeepTopPush on visual recognition datasets and two real-world applications. The first one selects a small number of molecules for further drug testing. The second one uses real malware data, where we detected 46\% malware at an extremely low false alarm rate of $10^{-5}$.
