When answering natural language questions over knowledge bases (KBs), incompleteness in the KB can naturally lead to many questions being unanswerable. While answerability has been explored in other QA settings, it has not been studied for QA over knowledge bases (KBQA). We first identify various forms of KB incompleteness that can result in a question being unanswerable. We then propose GrailQAbility, a new benchmark dataset, which systematically modifies GrailQA (a popular KBQA dataset) to represent all these incompleteness issues. Testing two state-of-the-art KBQA models (trained on original GrailQA as well as our GrailQAbility), we find that both models struggle to detect unanswerable questions, or sometimes detect them for the wrong reasons. Consequently, both models suffer significant loss in performance, underscoring the need for further research in making KBQA systems robust to unanswerability.

We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger in response to dynamic boundary conditions, eliminating the need to re-train. As a result, we achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations. This makes the approach very attractive for predictive maintenance of the heat exchanger in digital twin environments.

Deep neural networks (DNN) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples. However, from a deployment perspective, an ideal model is desired to (i) generate well-calibrated predictions for high-confidence samples with predicted probability say >0.95, and (ii) generate a higher proportion of legitimate high-confidence samples. To this end, we propose a novel regularization technique that can be used with classification losses, leading to state-of-the-art calibrated predictions at test time; From a deployment standpoint in safety-critical applications, only high-confidence samples from a well-calibrated model are of interest, as the remaining samples have to undergo manual inspection. Predictive confidence reduction of these potentially ``high-confidence samples'' is a downside of existing calibration approaches. We mitigate this by proposing a dynamic train-time data pruning strategy that prunes low-confidence samples every few epochs, providing an increase in "confident yet calibrated samples". We demonstrate state-of-the-art calibration performance across image classification benchmarks, reducing training time without much compromise in accuracy. We provide insights into why our dynamic pruning strategy that prunes low-confidence training samples leads to an increase in high-confidence samples at test time.

We are interested in neurosymbolic systems consisting of a high-level symbolic layer for explainable prediction in terms of human-intelligible concepts; and a low-level neural layer for extracting symbols required to generate the symbolic explanation. Real data is often imperfect meaning that even if the symbolic theory remains unchanged, we may still need to address the problem of mapping raw data to high-level symbols, each time there is a change in the data acquisition environment or equipment. Manual (re-)annotation of the raw data each time this happens is laborious and expensive; and automated labelling methods are often imperfect, especially for complex problems. NEUROLOG proposed the use of a semantic loss function that allows an existing feature-based symbolic model to guide the extraction of feature-values from raw data, using `abduction'. However, the experiments demonstrating the use of semantic loss through abduction appear to rely heavily on a domain-specific pre-processing step that enables a prior delineation of feature locations in the raw data. We examine the use of semantic loss in domains where such pre-processing is not possible, or is not obvious. We show that without any prior information about the features, the NEUROLOG approach can continue to predict accurately even with substantially incorrect feature predictions. We show also that prior information about the features in the form of even imperfect pre-training can help correct this situation. These findings are replicated on the original problem considered by NEUROLOG, without the use of feature-delineation. This suggests that symbolic explanations constructed for data in a domain could be re-used in a related domain, by `feature-adaptation' of pre-trained neural extractors using the semantic loss function constrained by abductive feedback.

类比推理问题挑战了连接主义者和符号AI系统，因为这些系统需要将背景知识，推理和模式识别的结合。符号系统摄入显式域知识并执行演绎推理，但它们对噪声敏感，并且需要输入以预设符号特征。另一方面，Connectionist系统可以直接摄入丰富的输入空间，例如图像，文本或语音，即使使用嘈杂的输入也可以识别模式。但是，Connectionist模型努力将明确的领域知识用于演绎推理。在本文中，我们提出了一个框架，将神经网络的模式识别能力与象征性推理和背景知识结合在一起，以解决一类类似推理问题，其中一组属性和可能的​​关系是已知的。我们从“神经算法推理”方法[DeepMind 2020]中汲取灵感，并通过（i）基于问题的象征模型学习分布式表示（ii）培训神经网络转化反映了关系的分布式表示形式。参与问题，最后（iii）培训神经网络编码器，从图像到（i）中的分布式表示。这三个要素使我们能够使用神经网络作为操纵分布式表示的基本功能执行基于搜索的推理。我们在乌鸦渐进式矩阵中的视觉类比问题上进行了测试，并在人类绩效中实现准确性竞争，在某些情况下，优于初始端到端神经网络方法的方法。尽管最近接受大规模训练的神经模型产生了SOTA，但我们的新型神经符号推理方法是该问题的有希望的方向，可以说是更笼统的，尤其是对于可用的域知识的问题。

物理知情的神经网络（PINN）已获得极大的流行，作为用于求解PDE的替代方法。尽管取得了经验的成功，但我们仍在对培训对梯度下降的这种约束的融合特性建立了解。众所周知，在没有明确的归纳偏见的情况下，神经网络可能会以样本有效的方式学习或近似简单且知名的功能。因此，从少数搭配点诱导的数值近似可能无法概括整个域。同时，符号形式可以表现出良好的概括，并具有可解释性为有用的副产品。但是，符号近似可能会同时简洁明了。因此，在这项工作中，我们探索了一种神经肌符号方法，以近似PDE的溶液。我们观察到我们的方法在几个简单的情况下起作用。我们说明了我们方法对Navier Stokes的功效：Kovasznay流动，其中有多个物理量的兴趣，该物理数量由非线性耦合PDE系统控制。域分裂现在已成为帮助PINNS近似复杂功能的流行技巧。我们观察到神经肌符号方法也可以帮助这种复杂的功能。我们在暂时变化的二维汉堡方程上展示了域分裂的辅助神经符号方法。最后，我们考虑了必须解决参数化PDE的PINN的情况，以改变初始条件和PDE系数的变化。超级核武器已证明有望克服这些挑战。我们表明，可以设计超启动的网络，以结合速度的好处和提高准确性。我们观察到，神经词近似值始终是1-2个数量级，而不是神经或符号近似值。

我们考虑一类视觉模拟推理问题，涉及发现输入/输出图像对相关的转换序列，以类似地改变未来输入。该程序综合任务可以通过符号搜索轻松解决。使用（Velickovic和Blundell 2021）的“神经模拟推理”方法的变化，Edw，例如，搜索一系列基本神经网络变换，其操纵从符号空间导出的分布式表示，输入图像直接编码。我们评估了我们的“神经原理”方法对具有看不见形状和位置的图像的程度。

在计算机视觉中探索的分销（OOD）检测良好的虽然，但在NLP分类的情况下已经开始较少尝试。在本文中，我们认为这些目前的尝试没有完全解决ood问题，并且可能遭受数据泄漏和所产生模型的校准差。我们呈现PNPOOD，通过使用最近提出的即插即用语言模型（Dathathri等，2020），通过域外样本生成进行数据增强技术来执行OOD检测。我们的方法产生靠近阶级边界的高质量辨别样本，从而在测试时间内进行准确的检测。我们展示了我们的模型优于预先样本检测的现有模型，并在20次新闻组文本和斯坦福情绪Teebank数据集上展示较低的校准错误（Lang，1995; Socheret al。，2013）。我们进一步突出显示了在EAC检测的先前尝试中使用的数据集进行了重要的数据泄露问题，并在新数据集中分享结果，以便无法遭受同样问题的检测。

State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary's essential information and form. We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed. We show that this model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30% on XSum, and that this model can be applied on top of another post-editor, improving entity precision by up to a total of 38%. We perform an extensive comparison of post-editing approaches that demonstrate trade-offs between factual consistency, informativeness, and grammaticality, and we analyze settings where post-editors show the largest improvements.

紧固件在确保机械的各个部位方面起着至关重要的作用。紧固件表面的凹痕，裂缝和划痕等变形是由材料特性和生产过程中设备的错误处理引起的。结果，需要质量控制以确保安全可靠的操作。现有的缺陷检查方法依赖于手动检查，该检查消耗了大量时间，金钱和其他资源；同样，由于人为错误，无法保证准确性。自动缺陷检测系统已证明对缺陷分析的手动检查技术有影响。但是，诸如卷积神经网络（CNN）和基于深度学习的方法之类的计算技术是进化方法。通过仔细选择设计参数值，可以实现CNN的全部电势。使用基于Taguchi的实验和分析设计，已经尝试在本研究中开发强大的自动系统。用于训练系统的数据集是为具有两个标记类别的M14尺寸螺母手动创建的：有缺陷且无缺陷。数据集中共有264张图像。所提出的顺序CNN的验证精度为96.3％，在0.001学习率下的验证损失为0.277。