An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal. Such techniques, including Classification Without Labels (CWoLa) and Simulation Assisted Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset. They cannot take advantage of commonly-available multiple datasets and thus cannot fully exploit available information. In this work, we propose generalizations of CWoLa and SALAD for settings where multiple reference datasets are available, building on weak supervision techniques. We demonstrate improved performance in a number of settings with realistic and synthetic data. As an added benefit, our generalizations enable us to provide finite-sample guarantees, improving on existing asymptotic analyses.

理想学识渊博的表示应显示可转移性和鲁棒性。监督对比学习（SUPCON）是一种训练准确模型的有前途的方法，但是当班级映射中的所有点符合相同的表示形式时，就会产生不会捕获这些属性的表示形式。最近的工作表明，“散布”这些表示可以改善它们，但是确切的机制知之甚少。我们认为，单独创建点差不足以进行更好的表示，因为差异对于班级的排列不变。取而代之的是，有必要正确的传播程度和破坏这种不变性的机制。我们首先证明，添加加权类条件的信息损失以控制传播程度。接下来，我们研究了三种破坏排列不变性的机制：使用约束编码器，添加类条件自动编码器并使用数据增强。我们表明，后两者鼓励在更现实的条件下与前者聚集潜在子类。使用这些见解，我们表明，在5个标准数据集中添加适当加权的集体条件infonce损失和一个班级条件自动编码器，以在5个标准数据集中进行粗到5分的转移，并在最差的组上进行4.7分，以达到11.1个升力。 3个数据集，将Celeba的最新时间设置为11.5分。

基础模型为使用开箱即用的嵌入和一些标签示例构建模型提供了令人兴奋的新范式。但是，尚不清楚如何在没有标记数据的情况下最好地应用基础模型。一种潜在的方法是将基础模型与弱监督框架融合在一起，该框架使用弱标签来源（预训练的模型，启发式方法，人群工人）来构建伪标记。挑战是建立一个最能利用基础模型和弱来源中可用信号的组合。我们提出了Liger，这是一种使用基础模型嵌入来改善现有弱监督技术的两个关键要素的组合。首先，我们通过分区嵌入空间和每分部分的源精度来产生较弱的源质量估计。其次，我们通过扩展嵌入空间中的源票来提高源覆盖范围。尽管基础模型具有黑盒的性质，但我们证明了表征我们的方法如何提高性能的结果，并证明了通过在嵌入空间中标签分布的平滑度来表明升降尺度。在六个基准的NLP和视频任务上，Liger的表现优于香草弱监督，弱监督的KNN和适配器的弱监督和适配器的表现为11.8分，而传统手工标签监督的KNN和适配器则以7.2分的监督。

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.

Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020 and 2021) as the source and target datasets, respectively. For each patient, the degree of knee OA was initially graded according to the MRI Osteoarthritis Knee Score (MOAKS) before being converted to binary OA phenotype labels. The proposed UDA pipeline included (a) pre-processing, which involved automatic segmentation and region-of-interest cropping; (b) source classifier training, which involved pre-training phenotype classifiers on the source dataset; (c) target encoder adaptation, which involved unsupervised adaption of the source encoder to the target encoder and (d) target classifier validation, which involved statistical analysis of the target classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was trained without UDA for comparison. Results: The target classifier trained with UDA achieved improved AUROC, sensitivity, specificity and accuracy for both knee OA phenotypes compared with the classifier trained without UDA. Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training. The results successfully demonstrated the advantages of the UDA approach in classification on small datasets.

Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%.

已知神经模型被过度参数化，最近的工作表明，稀疏的文本到语音（TTS）模型可以超过密集的模型。尽管已经为其他域提出了大量稀疏方法，但这种方法很少在TTS中应用。在这项工作中，我们试图回答以下问题：所选稀疏技术在性能和模型复杂性上的特征是什么？我们比较了Tacotron2基线和应用五种技术的结果。然后，我们通过自然性，清晰度和韵律来评估表现，同时报告模型规模和训练时间。与先前的研究相辅相成，我们发现在训练之前或期间进行修剪可以实现与训练后的修剪相似的性能，并且可以更快地进行培训，同时除去整个神经元降低了性能远不止于删除参数。据我们所知，这是比较文本到语音综合中稀疏范式的第一部作品。

这项研究提出了一种基于深度学习的超声（US）图像引导放射疗法的跟踪方法。拟议的级联深度学习模型由注意力网络，基于掩模区域的卷积神经网络（Mask R-CNN）和长期短期记忆（LSTM）网络组成。注意网络从美国图像到可疑的具有里程碑意义的运动区域，以减少搜索区域。然后，面膜R-CNN在减少区域中产生多个利益区域（ROI）建议，并通过三个网络头确定拟议的地标：边界框回归，提案分类和地标分段。 LSTM网络对连续的图像框架之间的时间关系建模，以进行边界框回归和建议分类。为了合并最终建议，根据顺序框架之间的相似性设计选择方法。该方法在肝脏美国跟踪数据集中测试了医疗图像计算和计算机辅助干预措施（MICCAI）2015年的挑战，其中有三位经验丰富的观察者注释了地标，以获得其平均位置。在24个鉴于我们具有地面真相的序列的24个序列上，所有地标的平均跟踪误差为0.65 +/- 0.56毫米，所有地标的误差均在2 mm之内。我们进一步测试了从测试数据集中的69个地标上提出的模型，该模型具有与训练模式相似的图像模式，从而导致平均跟踪误差为0.94 +/- 0.83 mm。我们的实验结果表明，我们提出的方法使用US图像跟踪肝解剖学地标的可行性和准确性，为放射治疗期间的主动运动管理提供了潜在的解决方案。

会话问题生成（CQG）是机器通过对话等人类（例如交互式阅读理解）的重要任务。与传统的单转交问题（SQG）相比，CQG更具挑战性的意义，即生成的问题不仅需要有意义，而且要与发生的对话历史保持一致。虽然先前的研究主要集中于如何建模对话的流量和对齐，但迄今为止，尚无对模型必需部分和历史的部分进行全面的研究。我们认为，缩短上下文和历史是至关重要的，因为它可以帮助该模型对对话的一致性进行更多优化。为此，我们提出了一个两阶段CQG框架COHS-CQG，该框架采用COHS模块来缩短输入的上下文和历史记录。特别是，COHS选择连续的句子，并根据其相关性得分通过顶级P策略转弯。我们的模型在答案感和答案环境中都可以在COQA上实现最先进的表演。

模拟和混合信号（AMS）电路设计仍然依赖于人类设计专业知识。机器学习一直通过用人工智能代替人类的体验来协助电路设计自动化。本文介绍了标签，这是一种从利用文本，自我注意力和图形的布局中学习电路表示的新范式。嵌入网络模型在无手动标签的情况下学习空间信息。我们向AMS电路学习介绍文本嵌入和自我注意的机制。实验结果表明，具有工业罚款技术基准的实例之间的布局距离的能力。通过在案例研究中显示有限数据的其他三个学习任务的转移性，可以验证电路表示的有效性：布局匹配预测，线长度估计和净寄生电容预测。