培训大型神经网络架构的快速增长带来了对划分策略的需要，例如通过使用数据，模型或管道并行性。通过程序基元越来越多地支持这些方法，但识别有效的分区策略需要昂贵的实验和专业知识。我们介绍了自动分区器的原型，它无缝集成到现有的编译器和现有用户工作流中。我们的分区使SPMD风格的并行性能够包含数据并行性和参数/激活分片。通过归纳策略和在平台独立的分区IR中搜索的组合，Automap可以恢复用于变压器层的专家分区策略，如Megatron分片。

现代大规模深度学习工作负载突出了在许多设备上并行执行的需要，以便将模型数据拟合到硬件加速器存储器中。在这些设置中，在计算期间可能需要数组再分配，但如果没有有效地完成，也可以成为瓶颈。在本文中，我们解决了在SPMD计算中重新分配多维阵列数据的问题，深度学习中最普遍的并行性形式。我们介绍了一种类型定向的方法来合成阵列再分配作为MPI式集体操作的序列。我们证明了我们的合成重新分发是内存高效的，并且不执行过多的数据传输。使用集体操作的SPMD计算的数组再分配也在XLA SPMD Partitioner的上下文中实现了一种用于跨加速器系统进行分区程序的生产级工具。我们评估我们对XLA实施的方法，并发现我们的方法提供了1.22美元的几何平均加速，最大加速度为高达5.7倍$，同时提供可提供的内存保证，使我们的系统特别吸引力 - 规模模型。

人工智能（AI），机器学习和深度学习（DL）方法在生物医学图像分析领域变得越来越重要。但是，为了利用此类方法的全部潜力，需要作为训练数据代表数量的实验获得的图像，其中包含大量手动注释对象。在这里，我们将语法（合成数据）介绍为一种新的方法，用于生成合成，光现实和高度复杂的生物医学图像作为DL系统的训练数据。我们在组织学切片中的肌肉纤维和结缔组织分析的背景下显示了方法的多功能性。我们证明，可以在以前看不见的现实世界数据上执行强大和专家级的细分任务，而无需仅使用合成训练数据进行手动注释。作为一种完全参数技术，我们的方法为生成对抗网络（GAN）构成了可解释的可控替代方案，并且有可能在显微镜及其他地区的各种生物医学应用中显着加速定量图像分析。

鲁棒性是机器学习（ML）分类器的基本支柱，实质上确定了它们的可靠性。因此，评估分类器鲁棒性的方法至关重要。在这项工作中，我们解决了评估腐败鲁棒性的挑战，该方式允许在给定数据集上可比性和解释性。我们提出了一种测试数据增强方法，该方法使用稳健性距离$ \ epsilon $从数据集中衍生的最小类分隔距离。由此产生的MSCR（平均统计损坏鲁棒性）允许对不同分类器在腐败鲁棒性方面进行特定于数据集的比较。 MSCR值是可以解释的，因为它代表了由于统计损坏而避免了准确性损失的分类器。在2D和图像数据上，我们表明度量标准反映了分类器鲁棒性的不同级别。此外，我们通过训练和测试不同级别的噪声测试分类器观察到分类器中意外的最佳精度。虽然研究人员经常在训练健壮的模型时经常报道准确性的重大权衡，但我们加强了这样一种观点，即准确性和腐败鲁棒性之间的权衡并不是固有的。我们的结果表明，通过简单数据增强，稳健性训练已经可以稍微提高准确性。

与标准命名实体识别（NER）相比，在历史文本中识别人，位置和组织是一个巨大的挑战。为了获得机器可读的语料库，通常需要扫描历史文本，并且需要执行光学特征识别（OCR）。结果，历史文献包含错误。此外，位置或组织等实体可以随着时间的推移而改变，这构成了另一个挑战。总体而言，历史文本带有几种特殊性，这些特殊性与现代文本有很大不同，并且在该领域几乎无法使用训练神经标记器的大型标记的Corpora。在这项工作中，我们通过培训大型历史语言模型来解决历史，英语，法语，瑞典语和芬兰语的历史文献。我们通过使用未标记的数据预处理语言模型来规避大量标记数据的需求。我们提出了Hmbert，这是一种历史多语言基于BERT的语言模型，并以多种不同大小的版本发布该模型。此外，我们通过解决下游NER作为今年HIPE-2022共享任务的一部分来评估HMBERT的能力，并提供详细的分析和见解。对于多种语言的经典评论粗粒ner挑战，我们的标记者Histeria的表现优于其他团队的三种语言中的其他团队的模型。

我们呈现了基于纯变压器的视频分类模型，在图像分类中最近的近期成功进行了借鉴。我们的模型从输入视频中提取了时空令牌，然后由一系列变压器层编码。为了处理视频中遇到的令牌的长序列，我们提出了我们模型的几种有效的变体，它们将输入的空间和时间维构建。虽然已知基于变换器的模型只有在可用的大型训练数据集时才有效，但我们展示了我们如何在训练期间有效地规范模型，并利用预先训练的图像模型能够在相对小的数据集上训练。我们进行彻底的消融研究，并在包括动力学400和600，史诗厨房，东西的多个视频分类基准上实现最先进的结果，其中 - 基于深度3D卷积网络的现有方法表现出优先的方法。为了促进进一步的研究，我们在https://github.com/google-research/scenic/tree/main/scenic/projects/vivit发布代码

Diversity Searcher is a tool originally developed to help analyse diversity in news media texts. It relies on a form of automated content analysis and thus rests on prior assumptions and depends on certain design choices related to diversity and fairness. One such design choice is the external knowledge source(s) used. In this article, we discuss implications that these sources can have on the results of content analysis. We compare two data sources that Diversity Searcher has worked with - DBpedia and Wikidata - with respect to their ontological coverage and diversity, and describe implications for the resulting analyses of text corpora. We describe a case study of the relative over- or under-representation of Belgian political parties between 1990 and 2020 in the English-language DBpedia, the Dutch-language DBpedia, and Wikidata, and highlight the many decisions needed with regard to the design of this data analysis and the assumptions behind it, as well as implications from the results. In particular, we came across a staggering over-representation of the political right in the English-language DBpedia.

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.

Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many physical invariances and symmetries can be incorporated into the kernel function to compensate for much larger datasets. So far, the scalability of this approach has however been hindered by its cubical runtime in the number of training points. While it is known, that iterative Krylov subspace solvers can overcome these burdens, they crucially rely on effective preconditioners, which are elusive in practice. Practical preconditioners need to be computationally efficient and numerically robust at the same time. Here, we consider the broad class of Nystr\"om-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods estimate the relevant subspace spanned by the kernel matrix columns using different strategies to identify a representative set of inducing points. Our comprehensive study covers the full spectrum of approaches, starting from naive random sampling to leverage score estimates and incomplete Cholesky factorizations, up to exact SVD decompositions.

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.