本文通过将深度递归编码器添加到具有深递归编码器（BERT-DRE）的伯爵，提供了一种深度神经阵列匹配（NLSM）。我们对模型行为的分析表明，BERT仍未捕获文本的全部复杂性，因此伯特顶部应用了一个深递归编码器。具有残留连接的三个Bi-LSTM层用于设计递归编码器，并在此编码器顶部使用注意模块。为了获得最终的载体，使用由平均值和最大池组成的池化层。我们在四个基准，SNLI，贝尔船，Multinli，Scitail和新的波斯宗教问题数据集上进行模型。本文侧重于改善NLSM任务中的BERT结果。在这方面，进行BERT-DRE和BERT之间的比较，并且显示在所有情况下，BERT-DRE优于伯特。宗教数据集的BERT算法实现了89.70％的精度，并且BERT-DRE架构使用相同的数据集提高了90.29％。

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is challenging and cannot be guaranteed in a finite time. We propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.

Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.

Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions. We present the safety assurance case in detail for Artificial Pancreas Systems (APS) as a representative application of learning-enabled MCPS, and provide a detailed analysis by implementing a deep neural network for the prediction in APS. We check the sufficiency of the ML data and analyze the correctness of the ML-based prediction using formal verification. Finally, we outline open research problems based on our experience in this paper.

Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, assessing credit risk, etc. This calls for handling massive incoming information with minimum response delay while ensuring fair and high quality decisions. Recent discrimination-aware learning methods are optimized based on overall accuracy. However, the overall accuracy is biased in favor of the majority class; therefore, state-of-the-art methods mainly diminish discrimination by partially or completely ignoring the minority class. In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes. Our proposed algorithm is simple, fast, and attains multi-objective optimization goals. To handle class imbalance and concept drifts, a dynamic instance weighting module is proposed, which gives more importance to recent instances and less importance to obsolete instances based on their membership in minority or majority class. We conducted experiments on a range of streaming and static datasets and deduced that our proposed methodology outperforms existing state-of-the-art fairness-aware methods in terms of both discrimination score and balanced accuracy.

神经科学方面的巨大努力正在努力绘制许多新物种的连接群，包括果蝇果蝇的接近完成。重要的是要问这些模型是否可以使人工智能受益。在这项工作中，我们提出了两个基本问题：（1）生物连接组可以在机器学习中提供的何处以及何时提供使用，（2）哪些设计原理对于提取连接组的良好表示是必要的。为此，我们将秀丽隐杆线虫线虫的运动电路转化为以不同水平的生物物理现实主义水平的人工神经网络，并评估了这些网络在运动和非运动行为任务上训练这些网络的结果。我们证明，生物物理现实主义不必维持使用生物回路的优势。我们还确定，即使没有保留确切的接线图，建筑统计数据也提供了有价值的先验。最后，我们表明，虽然秀丽隐杆线虫运动电路对运动问题提供了强大的感应偏见，但其结构可能会阻碍与运动无关的任务（例如视觉分类问题）。

尽管在时间序列重建的深度学习方法中取得了长足的进步，但由于其对优化损失的贡献可忽略不计，因此没有设计现有的方法来揭示具有微小信号强度的本地活动。但是，这种局部活动可以表示生理系统中重要的异常事件，例如额外的焦点触发心脏电波异常的传播。我们讨论了一种重建这种本地活动的新技术，尽管信号强度很小，但它是随后具有较大信号强度的全球活动的原因。我们的中心创新是通过明确建模并解开系统潜在的潜在隐藏内部干预措施的影响来解决此问题。在状态空间模型（SSM）的新型神经公式中，我们首先通过分别描述的相互作用的神经ODES系统引入潜在动力学的因果效应建模1）内部干预的连续时间动力学； 2）它对系统本地状态轨迹的影响。因为不能直接观察干预措施，而必须与观察到的后续效果脱离，所以我们整合了对系统的无天然干预动态的知识，并通过假设它是对实际观察到的差异来推断隐藏干预措施的推断和假设的无干预动态。我们证明了对重建异位焦点的提出框架的概念证明，从而破坏了从远程观察到正常心脏电气传播的过程。

知识蒸馏（KD）是压缩边缘设备深层分类模型的有效工具。但是，KD的表现受教师和学生网络之间较大容量差距的影响。最近的方法已诉诸KD的多个教师助手（TA）设置，该设置依次降低了教师模型的大小，以相对弥合这些模型之间的尺寸差距。本文提出了一种称为“知识蒸馏”课程专家选择的新技术，以有效地增强在容量差距问题下对紧凑型学生的学习。该技术建立在以下假设的基础上：学生网络应逐渐使用分层的教学课程来逐步指导，因为它可以从较低（较高的）容量教师网络中更好地学习（硬）数据样本。具体而言，我们的方法是一种基于TA的逐渐的KD技术，它每个输入图像选择单个教师，该课程是基于通过对图像进行分类的难度驱动的课程的。在这项工作中，我们凭经验验证了我们的假设，并对CIFAR-10，CIFAR-100，CINIC-10和Imagenet数据集进行了严格的实验，并在类似VGG的模型，Resnets和WideresNets架构上显示出提高的准确性。

在离岸部门以及科学界在水下行动方面的迅速发展，水下车辆变得更加复杂。值得注意的是，许多水下任务，包括对海底基础设施的评估，都是在自动水下车辆（AUV）的帮助下进行的。最近在人工智能（AI）方面取得了突破，尤其是深度学习（DL）模型和应用，这些模型和应用在各种领域都广泛使用，包括空中无人驾驶汽车，自动驾驶汽车导航和其他应用。但是，由于难以获得特定应用的水下数据集，它们在水下应用中并不普遍。从这个意义上讲，当前的研究利用DL领域的最新进步来构建从实验室环境中捕获的物品照片产生的定制数据集。通过将收集到的图像与包含水下环境的照片相结合，将生成的对抗网络（GAN）用于将实验室对象数据集转化为水下域。这些发现证明了创建这样的数据集的可行性，因为与现实世界的水下船体船体图像相比，所得图像与真实的水下环境非常相似。因此，水下环境的人工数据集可以克服因对实际水下图像的有限访问而引起的困难，并用于通过水下对象图像分类和检测来增强水下操作。

在本文中，我们提出了一个样本复杂性，以从嘈杂的样本中学习单纯形。给出了$ n $的数据集，其中包括i.i.d.样品从$ \ mathbb {r}^k $中的未知任意单纯形上的均匀分布中得出，其中假定样品被任意幅度的加性高斯噪声损坏。我们提出了一种策略，该策略可以输出一个单纯概率，总变化距离为$ \ epsilon + o \ left（\ mathrm {snr}^{ - 1} \ right）$从true Simplex中，对于任何$ \ Epsilon> 0 $。我们证明，要接近True Simplex，就足以拥有$ n \ ge \ tilde {o} \ left（k^2/\ epsilon^2 \ right）$ samples。在这里，SNR代表信噪比，可以看作是单纯形直径与噪声的标准偏差的比率。我们的证明是基于样品压缩技术的最新进步，这些进步已经显示出在高维高斯混合模型中的密度估计的紧密范围方面的承诺。