图像分类是计算机视觉中最重要的领域之一。当根据层次结构或分类学排列多级图像分类问题时，层次多标签分类适用于较小的图像分类问题。因此，分层分类模式通常在每个实例上提供多个类预测，从而期望这些模式反映图像类的结构相互关联。在本文中，我们提出了用于分层分类的多标签胶囊网络（ML-CAPSNET）。我们的ML-CAPSNET根据分层类标签树结构预测多个图像类。为此，我们提出了一个损失函数，该函数考虑了网络的多标签预测。结果，我们的ML-CAPSNET的训练方法使用粗到细的范式，同时与标签层次结构中的分类水平保持一致。我们还使用广泛可用的数据集执行实验，并将模型与文献其他地方的替代方案进行比较。在我们的实验中，我们的ML capsnet在这些替代方法方面产生了改善的余地。

点云过滤和正常估计是3D场中的两个基本研究问题。现有方法通常会单独执行正常的估计和过滤，并且经常表现出对噪声和/或无法保留尖锐几何特征（例如角和边缘）的敏感性。在本文中，我们提出了一种新颖的深度学习方法，以共同估计正态和过滤点云。我们首先引入了一个基于3D补丁的对比学习框架，并以噪声损坏为增强，以训练能够生成点云斑块的忠实表示的功能编码器，同时保持噪音的强大功能。这些表示由简单的回归网络消耗，并通过新的关节损失进行监督，同时估算用于过滤贴片中心的点正常和位移。实验结果表明，我们的方法同时支持这两个任务，并保留尖锐的功能和细节。通常，它在这两个任务上都胜过最先进的技术。

近年来，基于复杂的卷积神经网络架构的越来越复杂的方法一直在缓慢推动良好的基准数据集的性能。在本文中，我们返回返回检查真正需要这种复杂性。我们呈现RC-Net，一个完全卷积的网络，其中每层过滤器数量被优化，以减少特征重叠和复杂性。我们还使用跳过连接来将空间信息丢失保持为最小，通过将网络中的汇集操作保持到最小。在我们的实验中使用了两个公开的视网膜血管分段数据集。在我们的实验中，RC-Net是非常有竞争力的，表现优于替代方案的分割方法，具有两种甚至三个数量级的训练参数。

Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, particularly deep learning, solutions. However, in order to implement these solutions in production and enable widespread adoption, the scalability and transferability of deep learning methods have become a focus of research. While transfer learning has proven successful in many cases, particularly with computer vision and homogenous data inputs, it can be challenging to apply to heterogeneous data. Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data, and enables the transfer and scalability of deep learning-based statistical control methods in a general manner. Additionally, the cyclic interactions between the different parts of the model enable DBACS to not only adapt to the domains, but also match them. To the best of our knowledge, DBACS is the first deep learning approach to combine adaptation and matching for heterogeneous data settings. For comparison, this work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations by mapping data into correlated latent feature spaces. Finally, DBACS with its ability to adapt and match, is applied to a virtual metrology use case for an etching process run on different machine types in semiconductor manufacturing.

An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations. In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models. The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function. We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures - an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation.

Aliasing is a highly important concept in signal processing, as careful consideration of resolution changes is essential in ensuring transmission and processing quality of audio, image, and video. Despite this, up until recently aliasing has received very little consideration in Deep Learning, with all common architectures carelessly sub-sampling without considering aliasing effects. In this work, we investigate the hypothesis that the existence of adversarial perturbations is due in part to aliasing in neural networks. Our ultimate goal is to increase robustness against adversarial attacks using explainable, non-trained, structural changes only, derived from aliasing first principles. Our contributions are the following. First, we establish a sufficient condition for no aliasing for general image transformations. Next, we study sources of aliasing in common neural network layers, and derive simple modifications from first principles to eliminate or reduce it. Lastly, our experimental results show a solid link between anti-aliasing and adversarial attacks. Simply reducing aliasing already results in more robust classifiers, and combining anti-aliasing with robust training out-performs solo robust training on $L_2$ attacks with none or minimal losses in performance on $L_{\infty}$ attacks.

The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $\mathcal{O} (n)$ times using a quantum annealing device, exploring $\mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93\%$ on standard benchmark datasets.

Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task in the Continual Learning setting. In this work, we introduce a novel approach called SCALE (SCALing is Enough) to perform Compressed Replay in a framework for Anomaly Detection in Continual Learning setting. The proposed technique scales and compresses the original images using a Super Resolution model which, to the best of our knowledge, is studied for the first time in the Continual Learning setting. SCALE can achieve a high level of compression while maintaining a high level of image reconstruction quality. In conjunction with other Anomaly Detection approaches, it can achieve optimal results. To validate the proposed approach, we use a real-world dataset of images with pixel-based anomalies, with the scope to provide a reliable benchmark for Anomaly Detection in the context of Continual Learning, serving as a foundation for further advancements in the field.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.