当前的对比学习方法使用从大量转换列表（固定的超参数）中采样的随机转换来从未经注释的数据库中学习不变性。遵循以前引入少量监督的作品，我们提出了一个框架，以找到使用可区分转换网络的对比度学习的最佳转换。我们的方法在监督准确性和收敛速度方面都在低注释的数据制度下提高了性能。与以前的工作相反，转换优化不需要生成模型。转换的图像保留相关信息以解决监督任务，此处分类。在34000 2D切片的大脑磁共振图像和11200胸X射线图像上进行实验。在两个数据集（具有标记数据的10％）上，我们的模型比具有100％标签的完全监督模型获得了更好的性能。

通常认为CNN能够使用有关其接收领域内不同对象（例如其定向关系）的上下文信息。但是，这种能力的性质和限制从未得到充分探索。我们使用经过训练的标准U-NET探索特定类型的关系〜-定向〜-，以优化分割的跨透镜损失函数。我们按照借口细分任务训练该网络，需要取得成功的方向关系推理，并指出，凭借足够的数据和足够大的接收领域，它成功地学习了所提出的任务。我们进一步探讨了网络通过分析方向关系受到干扰的方案，并表明网络已经学会了使用这些关系来推理。

Selecting an effective training signal for tasks in natural language processing is difficult: collecting expert annotations is expensive, and crowd-sourced annotations may not be reliable. At the same time, recent work in machine learning has demonstrated that learning from soft-labels acquired from crowd annotations can be effective, especially when there is distribution shift in the test set. However, the best method for acquiring these soft labels is inconsistent across tasks. This paper proposes new methods for acquiring soft-labels from crowd-annotations by aggregating the distributions produced by existing methods. In particular, we propose to find a distribution over classes by learning from multiple-views of crowd annotations via temperature scaling and finding the Jensen-Shannon centroid of their distributions. We demonstrate that using these aggregation methods leads to best or near-best performance across four NLP tasks on out-of-domain test sets, mitigating fluctuations in performance when using the constituent methods on their own. Additionally, these methods result in best or near-best uncertainty estimation across tasks. We argue that aggregating different views of crowd-annotations as soft-labels is an effective way to ensure performance which is as good or better than the best individual view, which is useful given the inconsistency in performance of the individual methods.

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.

The analysis of digitized historical manuscripts is typically addressed by paleographic experts. Writer identification refers to the classification of known writers while writer retrieval seeks to find the writer by means of image similarity in a dataset of images. While automatic writer identification/retrieval methods already provide promising results for many historical document types, papyri data is very challenging due to the fiber structures and severe artifacts. Thus, an important step for an improved writer identification is the preprocessing and feature sampling process. We investigate several methods and show that a good binarization is key to an improved writer identification in papyri writings. We focus mainly on writer retrieval using unsupervised feature methods based on traditional or self-supervised-based methods. It is, however, also comparable to the state of the art supervised deep learning-based method in the case of writer classification/re-identification.

对AI的道德影响和值得信赖系统的设计的研究需要分析使用AI系统的方案，这与“用例”的软件工程概念和“预期目的”法律术语有关。但是，没有用于涵盖使用，范围，功能要求和AI系统风险的用例文档的标准方法。在这项工作中，我们为AI用例提出了一种新颖的文档方法，特别关注情感计算领域。我们的方法基于对研究文献中记录的用例信息需求的评估以及最近提议的AI欧洲监管框架。通过此评估，我们采用并调整了统一的建模语言（UML），在过去的二十年中，这主要由软件工程师使用。然后，每个用例都由UML图和一个结构化表表示，我们提供了一组示例，说明了其在几种情感计算方案中的应用。

目前，自然语言理解（NLU）中最根本的两个挑战是：（a）如何以“正确”的原因确定基于深度学习的模型是否在NLU基准上得分很高；（b）了解这些原因甚至是什么。我们研究了关于两个语言“技能”的阅读理解模型的行为：核心分辨率和比较。我们为从系统中预期的推理步骤提出了一个定义，该系统将“缓慢阅读”，并将其与各种大小的贝特家族的五个模型的行为进行比较，这是通过显着分数和反事实解释观察到的。我们发现，对于比较（而不是核心），基于较大编码器的系统更有可能依靠“正确”的信息，但即使他们在概括方面也很难，表明他们仍然学习特定的词汇模式，而不是比较的一般原则。

我们介绍了在Neurips'22接受的Chalearn Meta学习系列中的新挑战的设计和基线结果，重点是“跨域”元学习。元学习旨在利用从以前的任务中获得的经验，以有效地解决新任务（即具有更好的性能，较少的培训数据和/或适度的计算资源）。尽管该系列中的先前挑战集中在域内几乎没有学习问题，但目的是有效地学习n-way K-shot任务（即N级培训示例的N班级分类问题），这项竞赛挑战了参与者的解决方案。从各种领域（医疗保健，生态学，生物学，制造业等）提出的“任何通道”和“任何镜头”问题，他们是为了人道主义和社会影响而被选为。为此，我们创建了Meta-Album，这是来自10个域的40个图像分类数据集的元数据，从中，我们从中以任何数量的“方式”（在2-20范围内）和任何数量的“镜头”来解释任务”（在1-20范围内）。竞争是由代码提交的，在Codalab挑战平台上进行了完全盲目测试。获奖者的代码将是开源的，从而使自动化机器学习解决方案的部署可以在几个域中进行几次图像分类。

诊断出红斑的偏头膜（EM）皮肤病变，使用深度学习技术的莱姆病最常见的早期症状可以有效预防长期并发症。现有的基于深度学习的EM识别的作品仅由于缺乏与相关患者数据相关的莱姆病相关图像的数据集，因此仅利用病变图像。医师依靠患者有关皮肤病变背景的信息来确认其诊断。为了协助深度学习模型，根据患者数据计算出的概率分数，这项研究引起了15位医生的意见。对于启发过程，准备了一份与EM相关的问题和可能的答案的问卷。医生为问题的不同答案提供了相对权重。我们使用基于高斯混合物的密度估计将医生评估转换为概率得分。为了引起概率模型验证，我们利用了形式的概念分析和决策树。引起的概率得分可用于使基于图像的深度学习莱姆病预扫描剂稳健。

脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。