知识图嵌入研究主要集中于学习知识图的连续表示链接预测问题。最近开发的框架可以有效地应用于研究相关的应用中。但是，这些框架无法满足现实应用程序的许多要求。随着知识图的大小的增长，在这些框架中，将计算从商品计算机转移到一组计算机变得更具挑战性。查找合适的高参数设置W.R.T.时间和计算预算留给从业者。此外，尽管持续学习在许多现实世界（深）学习驱动的应用中，持续学习在知识图嵌入框架中的持续学习方面通常被忽略。可以说，这些局限性解释了缺乏大型知识图的公开知识图嵌入模型。我们以框架的框架，pytorch闪电和拥抱面的框架开发了一个框架，以用硬件 - 静态方式计算大规模知识图的嵌入，以解决与真实应用规模有关的现实世界挑战。我们提供框架的开源版本以及具有超过11.4 B参数的预训练模型的枢纽。

课堂表达学习是可解释的监督机器学习的分支，越来越重要。在描述逻辑中的类表达式学习的大多数现有方法是搜索算法或基于硬规则的。特别地，基于细化运营商的方法遭受可扩展性问题，因为它们依赖于启发式功能来探索每个学习问题的大搜索空间。我们提出了一系列新的方法，我们配合了合成方法。此系列的实例是从提供的示例中直接计算类表达式。因此，它们不受基于搜索方法的运行时限制，也不存在于基于硬规则的方法的缺乏灵活性。我们研究了这种新型方法的三个实例，该方法使用轻量级神经网络架构从积极的例子组合中综合类表达式。他们对四个基准数据集的评估结果表明，它们可以在平均水平上有效地合成相对于输入示例的高质量类表达。此外，与最先进的方法的比较Celoe和Eltl表明我们在大型本体中实现了更好的F措施。为了重现性目的，我们提供了我们的实施以及在HTTPS://github.com/conceptLengtlearner/nces的公共Github存储库中的预先训练模型

知识图中的节点是一个重要任务，例如，预测缺失类型的实体，预测哪些分子导致癌症，或预测哪种药物是有前途的治疗候选者。虽然黑匣子型号经常实现高预测性能，但它们只是hoc后和本地可解释的，并且不允许学习模型轻松丰富域知识。为此，已经提出了学习描述了来自正和否定示例的逻辑概念。然而，学习这种概念通常需要很长时间，最先进的方法为文字数据值提供有限的支持，尽管它们对于许多应用是至关重要的。在本文中，我们提出了Evolearner - 学习ALCQ（D）的进化方法，它是与合格基数限制（Q）和数据属性配对的补充（ALC）的定语语言和数据属性（D）。我们为初始群体贡献了一种新颖的初始化方法：从正示例开始（知识图中的节点），我们执行偏见随机散步并将它们转换为描述逻辑概念。此外，我们通过在决定分割数据的位置时，通过最大化信息增益来提高数据属性的支持。我们表明，我们的方法在结构化机器学习的基准框架SML - 台阶上显着优于现有技术。我们的消融研究证实，这是由于我们的新颖初始化方法和对数据属性的支持。

基于细化运算符的概念学习方法探索部分有序的解决方案空间来计算概念，这些空间用作个体的二进制分类模型。然而，这些方法探索的概念的数量可以很容易地增长到数百万的复杂学习问题。这通常会导致不切实际的运行时间。我们建议通过预测解决方案空间探索前的目标概念的长度来缓解这个问题。通过这些手段，我们可以在概念学习期间修剪搜索空间。为了实现这一目标，我们比较四个神经结构，并在四个基准上进行评估。我们的评估结果表明，经常性的神经网络架构在概念长度预测中表现最佳，宏F-MEARY从38％到92％。然后，我们扩展了eloe算法 - 学习ALC概念 - 我们的概念长度预测器。我们的扩展会产生算法剪辑。在我们的实验中，夹子比ALC的其他最先进的概念学习算法速度至少为7.5倍 - 包括Celoe - 并且在4个数据集中学习的3个概念的F-Peasure中实现了重大改进。为了重现性，我们在HTTPS://github.com/conceptlencthLearner/learnlencths中提供我们在公共Github存储库中的实现

知识图形嵌入研究主要集中在两个最小的规范部门代数，$ \ mathbb {r} $和$ \ mathbb {c} $。最近的结果表明，四元增值嵌入的三线性产品可以是解决链路预测的更有效手段。此外，基于真实嵌入的卷曲的模型通常会产生最先进的链路预测结果。在本文中，我们调查了一种卷积操作的组成，具有超量用乘法。我们提出了四个方法qmult，amult，convic和convo来解决链路预测问题。 Qmult和Omult可以被视为先前最先进方法的四元数和octonion扩展，包括Distmult和复杂。 Convic和Convo在Qmult和Omlult上建立在剩余学习框架的方式中包括卷积操作。我们在七个链路预测数据集中评估了我们的方法，包括WN18RR，FB15K-237和YAGO3-10。实验结果表明，随着知识图的规模和复杂性的增长，学习超复分价值的矢量表示的益处变得更加明显。 Convo优于MRR的FB15K-237上的最先进的方法，命中@ 1并点击@ 3，而Qmult，Omlult，Convic和Convo在所有度量标准中的Yago3-10上的最终倾斜的方式。结果还表明，通过预测平均可以进一步改善链路预测性能。为了培养可重复的研究，我们提供了开源的方法，包括培训和评估脚本以及佩戴型模型。

With the proliferation of deep generative models, deepfakes are improving in quality and quantity everyday. However, there are subtle authenticity signals in pristine videos, not replicated by SOTA GANs. We contrast the movement in deepfakes and authentic videos by motion magnification towards building a generalized deepfake source detector. The sub-muscular motion in faces has different interpretations per different generative models which is reflected in their generative residue. Our approach exploits the difference between real motion and the amplified GAN fingerprints, by combining deep and traditional motion magnification, to detect whether a video is fake and its source generator if so. Evaluating our approach on two multi-source datasets, we obtain 97.17% and 94.03% for video source detection. We compare against the prior deepfake source detector and other complex architectures. We also analyze the importance of magnification amount, phase extraction window, backbone network architecture, sample counts, and sample lengths. Finally, we report our results for different skin tones to assess the bias.

Though impressive success has been witnessed in computer vision, deep learning still suffers from the domain shift challenge when the target domain for testing and the source domain for training do not share an identical distribution. To address this, domain generalization approaches intend to extract domain invariant features that can lead to a more robust model. Hence, increasing the source domain diversity is a key component of domain generalization. Style augmentation takes advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. However, all previous works ignored the correlation between different feature channels or only limited the style augmentation through linear interpolation. In this work, we propose a novel augmentation method, called \textit{Correlated Style Uncertainty (CSU)}, to go beyond the linear interpolation of style statistic space while preserving the essential correlation information. We validate our method's effectiveness by extensive experiments on multiple cross-domain classification tasks, including widely used PACS, Office-Home, Camelyon17 datasets and the Duke-Market1501 instance retrieval task and obtained significant margin improvements over the state-of-the-art methods. The source code is available for public use.

One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning by: i) aligning the EEG trials from different subjects in the Euclidean-space, ii) tailoring the techniques of deep learning for the scalograms of EEG signals to capture better frequency localization for low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt (a modernized ResNet architecture which supersedes state-of-the-art (SOTA) image classification models) as the backbone network via adaptive finetuning. On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we benchmark our method against SOTA via cross-subject validation and demonstrate improved accuracy in cognitive activity classification along with better generalizability across cohorts.

Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations.

The development of deep learning based image representation learning (IRL) methods has attracted great attention in the context of remote sensing (RS) image understanding. Most of these methods require the availability of a high quantity and quality of annotated training images, which can be time-consuming and costly to gather. To reduce labeling costs, publicly available thematic maps, automatic labeling procedures or crowdsourced data can be used. However, such approaches increase the risk of including label noise in training data. It may result in overfitting on noisy labels when discriminative reasoning is employed as in most of the existing methods. This leads to sub-optimal learning procedures, and thus inaccurate characterization of RS images. In this paper, as a first time in RS, we introduce a generative reasoning integrated label noise robust representation learning (GRID) approach. GRID aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels. To this end, we first integrate generative reasoning into discriminative reasoning through a variational autoencoder. This allows our approach to automatically detect training samples with noisy labels. Then, through our label noise robust hybrid representation learning strategy, GRID adjusts the whole learning procedure for IRL of these samples through generative reasoning and that of the other samples through discriminative reasoning. Our approach learns discriminative image representations while preventing interference of noisy labels during training independently from the IRL method. Thus, unlike the existing methods, GRID does not depend on the type of annotation, label noise, neural network, loss or learning task, and thus can be utilized for various RS image understanding problems. Experimental results show the effectiveness of GRID compared to state-of-the-art methods.