Neural networks are becoming increasingly popular in applications, but our mathematical understanding of their potential and limitations is still limited. In this paper, we further this understanding by developing statistical guarantees for sparse deep learning. In contrast to previous work, we consider different types of sparsity, such as few active connections, few active nodes, and other norm-based types of sparsity. Moreover, our theories cover important aspects that previous theories have neglected, such as multiple outputs, regularization, and l2-loss. The guarantees have a mild dependence on network widths and depths, which means that they support the application of sparse but wide and deep networks from a statistical perspective. Some of the concepts and tools that we use in our derivations are uncommon in deep learning and, hence, might be of additional interest.

学习分配的尾巴行为是一个众所周知的困难问题。从定义上讲，尾部的样品数量很小，深层生成模型（例如归一化流量）倾向于集中于学习分布的身体。在本文中，我们专注于提高归一化流以正确捕获尾巴行为的能力，从而形成更准确的模型。我们证明，可以通过其基本分布的边缘的尾巴来控制自回归流的边际尾巴。这种理论上的见解使我们获得了一种基于灵活的基础分布和数据驱动线性层的新型流量。经验分析表明，所提出的方法提高了准确性（尤其是在分布的尾巴上），并能够生成重尾数据。我们证明了它在天气和气候示例中的应用，其中捕获尾巴行为至关重要。

由于治疗益处和减轻劳动密集型工作的能力，在临床应用中使用康复机器人技术的重要性提高了。但是，他们的实际效用取决于适当的控制算法的部署，这些算法根据每个患者的需求来适应任务辅助的水平。通常，通过临床医生的手动调整来实现所需的个性化，这很麻烦且容易出错。在这项工作中，我们提出了一种新颖的在线学习控制体系结构，能够在运行时个性化控制力量。为此，我们通过以前看不见的预测和更新率来部署基于高斯流程的在线学习。最后，我们在一项实验用户研究中评估了我们的方法，在该研究中，学习控制器被证明可以提供个性化的控制，同时还获得了安全的相互作用力。

深度学习中使用的数据是臭名昭着的问题。例如，数据通常与不同的源组合，很少清洁和彻底清洁和审核，有时故意损坏。在“对抗攻击的标签”下，有意腐败靶向算法的弱斑。相比之下，已经研究了反映了有限的数据质量的可争应性更常见的腐败情况。这种“随机”损坏是由于测量误差，不可靠的源，方便采样等。这些腐败在深度学习中是常见的，因为根据严格的协议很少收集数据 - 与古典统计的某些部分的正式数据收集强烈对比。本文涉及这种腐败。我们介绍了一种充满活力的方法，通过最近的洞察中学到中位数和Le Cam的原则，我们表明该方法可以容易地实施，我们证明它在实践中表现得很好。总之，我们认为，我们的方法是基于最小二乘和交叉熵损失的标准参数训练的非常有前途的替代方案。

现代技术正在生成越来越多的数据。利用这些数据需要既有统计学上的声音又有效率的方法。通常，统计和计算方面会分别处理。在本文中，我们提出了一种在正规化估计的背景下纠缠这两个方面的方法。将我们的方法应用于稀疏和小组的回归，我们表明它可以在统计和计算上对标准管道进行改进。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.

Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system.

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems. The objective of this article is to introduce practitioners and researchers from the field of Information Systems (IS) to data-centric AI. We define relevant terms, provide key characteristics to contrast the data-centric paradigm to the model-centric one, and introduce a framework for data-centric AI. We distinguish data-centric AI from related concepts and discuss its longer-term implications for the IS community.