基于树的合奏以其出色的性能而闻名，其分类和回归问题以特征向量为特征，这些特征向量由来自各个范围和域的混合型变量表示。但是，考虑回归问题，它们主要旨在提供确定性的响应，或者用高斯分布来建模输出的不确定性。在这项工作中，我们介绍了TreeFlow，这是基于树的方法，结合了使用树形合奏和使用标准化流量的灵活概率分布进行建模的功能的好处。该解决方案的主要思想是将基于树的模型用作特征提取器，并将其与标准化流量的条件变体组合。因此，我们的方法能够为回归输出建模复杂分布。我们评估了针对具有不同体积，特征特征和目标维度的挑战回归基准的提议方法。与基于树的回归基线相比，我们在具有非高斯目标分布的数据集上获得了SOTA结果。

现代生成型号在包括图像或文本生成和化学分子建模的各种任务中获得优异的品质。然而，现有方法往往缺乏通过所要求的属性产生实例的基本能力，例如照片中的人的年龄或产生的分子的重量。包含此类额外的调节因子将需要重建整个架构并从头开始优化参数。此外，难以解除选定的属性，以便仅在将其他属性中执行不变的同时执行编辑。为了克服这些限制，我们提出插件（插件生成网络），这是一种简单而有效的生成技术，可以用作预先训练的生成模型的插件。我们的方法背后的想法是使用基于流的模块将纠缠潜在的潜在表示转换为多维空间，其中每个属性的值被建模为独立的一维分布。因此，插件可以生成具有所需属性的新样本，以及操作现有示例的标记属性。由于潜在代表的解散，我们甚至能够在数据集中的稀有或看不见的属性组合生成样本，例如具有灰色头发的年轻人，有妆容的男性或胡须的女性。我们将插入与GaN和VAE模型组合并将其应用于图像和化学分子建模的条件生成和操纵。实验表明，插件保留了骨干型号的质量，同时添加控制标记属性值的能力。

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.

Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets. With the increasing number of AutoML algorithms, deciding which would best suit a given problem becomes increasingly more work. Therefore, it is essential to use complex and challenging benchmarks which would be able to differentiate the AutoML algorithms from each other. This paper compares the performance of four different AutoML algorithms: Tree-based Pipeline Optimization Tool (TPOT), Auto-Sklearn, Auto-Sklearn 2, and H2O AutoML. We use the Diverse and Generative ML benchmark (DIGEN), a diverse set of synthetic datasets derived from generative functions designed to highlight the strengths and weaknesses of the performance of common machine learning algorithms. We confirm that AutoML can identify pipelines that perform well on all included datasets. Most AutoML algorithms performed similarly without much room for improvement; however, some were more consistent than others at finding high-performing solutions for some datasets.

我们提出了一种有效的正则化战略（CW-TALAR），用于解决持续的学习问题。它使用由在由所有任务共享的底层神经网络的目标层上定义的两个概率分布之间的校准术语，该概率分布在由所有任务共享的底层神经网络的目标层，以及用于建模输出数据表示的克拉米 - WOLD发生器的简单架构。我们的策略在学习新任务时保留了目标层分发，但不需要记住以前的任务的数据集。我们执行涉及几个常见监督框架的实验，该框架证明了CW-TALAR方法的竞争力与一些现有的现有最先进的持续学习模型相比。