在科学,计算和工程学中,黑盒是一种设备,系统或对象,可以根据其输入和输出(或传输特性)对其进行查看,而无需对其内部工作有任何了解。 它的实现是“不透明的”(黑色)。 几乎任何事物都可以被称为黑盒:晶体管,引擎,算法,人脑,机构或政府。为了使用典型的“黑匣子方法”来分析建模为开放系统的事物,仅考虑刺激/响应的行为,以推断(未知)盒子。 该黑匣子系统的通常表示形式是在该方框中居中的数据流程图。黑盒的对立面是一个内部组件或逻辑可用于检查的系统,通常将其称为白盒(有时也称为“透明盒”或“玻璃盒”)。

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主题: Imitation Attacks and Defenses for Black-box Machine Translation Systems

摘要: 我们考虑一个寻求窃取黑盒机器翻译(MT)系统的对手,以获取经济利益或排除模型错误。我们首先表明,黑盒机器翻译系统可以通过使用单语句子和训练模型来模拟它们的输出来窃取。通过模拟实验,我们证明了即使模仿模型的输入数据或架构与受害者不同,MTmodel的窃取也是可能的。应用这些思想,我们在高资源和低资源语言对上训练了三个生产MT系统的0.6 BLEU以内的模仿模型。然后,我们利用模仿模型的相似性将对抗性示例转移到生产系统。我们使用基于梯度的攻击,这些攻击会暴露输入,从而导致语义错误的翻译,内容丢失和庸俗的模型输出。为了减少这些漏洞,我们提出了一种防御措施,该防御措施会修改翻译输出,从而误导了模仿模型优化的防御措施。这种防御降低了仿真模型BLEU的性能,并降低了BLEU的攻击传输速率和推理速度。

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Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point estimates, such as the conditional mean of the underlying probability distribution, or confidence intervals, forecasting complete probability density functions allows to investigate the impact on operational metrics, which are important to define the business strategy, over the full range of the expected demand. Whereas metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare, and this work proposes new techniques for both qualitative and quantitative evaluation methods. Using the supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted such that each prediction is fully explainable. This is of particular importance for practitioners, as it allows to avoid "black-box" models and understand the contributing factors for each individual prediction. Another crucial aspect in terms of both explainability and generalizability of demand forecasting methods is the limitation of the influence of temporal confounding, which is prevalent in most state of the art approaches.

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