同质性是描述边缘连接相似节点的趋势的图形属性。相反称为异性。尽管同质性对于许多现实世界网络是自然的，但也有没有此属性的网络。人们通常认为，标准消息的图形神经网络（GNNS）在非双性图形上表现不佳，因此此类数据集需要特别注意。尽管为异性图开发图表的学习方法已经付出了很多努力，但尚无普遍同意同质的措施。但是，在文献中使用了几种测量同质性的指标，但是，我们表明所有这些度量都有关键的缺点，以阻止不同数据集之间的同质级别比较。我们将理想的属性形式化，以进行适当的同质度量，并展示如何将有关分类绩效指标属性的现有文献与我们的问题联系起来。在这样做时，我们找到了一种措施，我们称调整后的同质性比现有同质措施更满足所需的特性。有趣的是，该措施与两个分类性能指标有关 - 科恩的kappa和马修斯相关系数。然后，我们超越了同质性的二分法，并提出了一种新的属性，我们称之为标签信息性（LI），该属性表征了邻居标签提供有关节点标签的信息的数量。从理论上讲，我们表明LI在具有不同数量的类和类大小平衡的数据集中相当。通过一系列实验，我们表明LI是对数据集上GNN的性能的更好预测指标，而不是同质性。我们证明了Li解释了为什么GNN有时可以在异性数据集上表现良好 - 这是文献中最近观察到的现象。

目前，最新的表格数据深度学习模型与基于决策树（GBDT）的传统ML模型竞争。与GBDT不同，深层模型可以从训练预处理中受益，这是视觉和NLP的DL的主力。对于表格问题，提出了几种预处理的方法，但是尚不完全清楚训练是否提供一致的明显改进以及应使用哪种方法，因为这些方法通常不相互比较或比较仅限于最简单的MLP体系结构。在这项工作中，我们旨在确定可以将可以普遍应用于不同数据集和体系结构的表格DL模型的最佳实践。在我们的发现中，我们表明，在预训练阶段使用对象目标标签对下游性能是有益的，并提倡几个目标意识到的预处理目标。总体而言，我们的实验表明，正确进行预处理可显着提高表格DL模型的性能，这通常会导致其优越性比GBDT。

去噪扩散概率模型最近获得了很多研究的关注，因为它们优于GAN，以及目前提供最先进的生成性能。扩散模型的卓越性能使它们在若干应用中为它们提供了吸引人的工具，包括尿素，超分辨率和语义编辑。在本文中，我们证明扩散模型也可以用作语义分割的仪器，特别是当标记数据稀缺时的设置中。特别地，对于几种预训练的扩散模型，我们研究了从执行反向扩散过程的马尔可夫步骤的网络的中间激活。我们表明这些激活有效地捕获了来自输入图像的语义信息，并且看起来是分割问题的优异像素级表示。基于这些观察，我们描述了一种简单的分段方法，即使仅提供了几种训练图像也可以工作。我们的方法显着优于若干数据集的现有替代品，以获得相同数量的人类监督。

高保真语义图像编辑的最新进展依赖于最先进的生成模型的概述潜在的潜在空间，例如风格。具体而言，最近的作品表明，通过线性偏移以及潜在方向，可以实现面部图像中的属性的体面可控性。几个最近的方法解决了这种方向的发现，隐含地假设最先进的GAN学习潜在空间，具有固有的线性可分离属性分布和语义矢量算术属性。在我们的工作中，我们表明，作为培训神经颂歌的流动实现的非线性潜在的代码操纵对于许多具有更复杂的非纹理变化因子的实用非面孔图像域有益。特别是，我们调查具有已知属性的大量数据集，并证明某些属性操作仅具有线性移位的挑战。

关于表格数据深度学习的现有文献提出了广泛的新颖架构，并在各种数据集中报告竞争结果。然而，所提出的模型通常不适合彼此相比，并且现有的作品通常使用不同的基准和实验协议。因此，对于研究人员和从业者来说，目前尚不清楚模特表现最佳。此外，该领域仍然缺乏有效的基线，即易于使用的模型，可以在不同问题上提供竞争性能。在这项工作中，我们通过识别两个简单而强大的深层架构，执行表格数据的DL架构的主要系列的概述。第一个是类似Reset的架构，结果是一个强大的基线，在前的作品中经常丢失。第二种模型是我们简单地适应变压器体系结构的表格数据，这比大多数任务更优于其他解决方案。在相同的培训和调整协议下，这两种模型都与许多现有架构上的许多现有架构进行了比较。我们还将最佳DL模型与渐变提升决策树进行比较，并得出结论仍然没有普遍卓越的解决方案。

The latent spaces of GAN models often have semantically meaningful directions.Moving in these directions corresponds to humaninterpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently performed in a supervised manner, requiring human labels, pretrained models, or some form of self-supervision. These requirements severely restrict a range of directions existing approaches can discover.In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. By a simple model-agnostic procedure, we find directions corresponding to sensible semantic manipulations without any form of (self-)supervision. Furthermore, we reveal several non-trivial findings, which would be difficult to obtain by existing methods, e.g., a direction corresponding to background removal. As an immediate practical benefit of our work, we show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection. The implementation of our method is available online 1 .

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

This report summarises the outcomes of a systematic literature search to identify Bayesian network models used to support decision making in healthcare. After describing the search methodology, the selected research papers are briefly reviewed, with the view to identify publicly available models and datasets that are well suited to analysis using the causal interventional analysis software tool developed in Wang B, Lyle C, Kwiatkowska M (2021). Finally, an experimental evaluation of applying the software on a selection of models is carried out and preliminary results are reported.

自动临床标题生成问题被称为建议模型，将额叶X射线扫描与放射学记录中的结构化患者信息结合在一起。我们将两种语言模型结合在一起，即表演 - 泰尔和GPT-3，以生成全面和描述性的放射学记录。这些模型的建议组合产生了文本摘要，其中包含有关发现的病理，其位置以及将每个病理定位在原始X射线扫描中的每个病理的2D热图。提出的模型在两个医学数据集（Open-I，Mimic-CXR和通用MS-Coco）上进行了测试。用自然语言评估指标测量的结果证明了它们对胸部X射线图像字幕的有效适用性。