可以通过定期训练数据的生成建模或通过对负面训练数据进行区分来构想异常检测。这两种方法表现出不同的故障模式。因此，混合算法提出了一个有吸引力的研究目标。不幸的是，密集的异常检测需要翻译均衡和非常大的输入分辨率。这些要求取消了所有以前的混合方法，我们的最佳知识。因此，我们设计了一种基于重新解释的歧视liogits的新型混合算法，作为非标准化关节分布的对数$ \ hat {p}（\ mathbf {x}，\ mathbf {y}）$。我们的模型建立在共享卷积表示形式的基础上，我们从中恢复了三个密集的预测：i）封闭式类后$ p（\ mathbf {y} | \ mathbf {x}）$，ii）数据集posterior $ p（d_ {in} | \ mathbf {x}）$，iii）不正常的数据可能性$ \ hat {p}（\ mathbf {x}）$。后两个预测均受标准培训数据和通用负面数据集的培训。我们将这两个预测融合到混合异常评分中，该评分允许在大型自然图像上进行密集的开放式识别。我们仔细设计了针对数据可能性的自定义损失，以避免通过不可降低常规固定$ z（\ theta）$进行反向传播。实验评估了我们对标准密集异常检测基准的贡献，以及开放式MIOU的贡献，这是一种新颖的开放式开放式性能的新颖指标。尽管在标准语义分段基线上忽略了可忽视的计算间接费用，但我们的提交表现达到了最先进的性能。

标准机器学习无法容纳不属于培训分配的输入。由此产生的模型通常会产生自信不正确的预测，这可能导致破坏性后果。在密集预测的上下文中，该问题特别要求，因为输入图像可以部分是异常的。以前的工作通过对混合内容图像的鉴别培训解决了致密的异常检测。我们将这种方法与合成阴性贴片扩展，同时实现高入的似然性和均匀的辨别预测。由于其出色的分布覆盖范围和能力以不同的分辨率产生样品，我们会产生具有正常化流动的合成底片。我们还建议根据主要的信息理论标准来检测异常，这可以通过培训和推理一致地应用。结果模型在标准基准测试和数据集中设置了新技术，尽管计算开销最小，但避免辅助负数据。

归一化流量是输入和潜在表示之间的基础映射，具有完全分解的分布。由于精确的可能性估值和有效的抽样，它们非常有吸引力。然而，由于杀硅约束限制了模型宽度，因此它们的有效容量通常不足。我们通过逐渐填充噪音的中间表示来解决此问题。我们根据先前可逆的单位预处理噪声，我们将其描述为交叉单元耦合。我们可逆的发光模块通过融合具有腹部自我关注的密集连接块来提高模型表达性。我们将我们的体系结构称为致密流，因为跨单元和模块内联轴器都依赖于密集的连接。实验表现出显着的改善，因为拟议的贡献和揭示了中等计算预算下的最先进的密度估算。

This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction to a different level of detail that is both consistent with the history of observed data and more effective in solving a given task.

Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in any other growing subfield, patience seems to be a virtue since significant progress on integrating notions from both fields takes time, yet, major challenges such as the lack of ground truth benchmarks or a unified perspective on classical problems such as computer vision seem to hinder the momentum of the research movement. This present work exemplifies how the Pearl Causal Hierarchy (PCH) can be understood on image data by providing insights on several intricacies but also challenges that naturally arise when applying key concepts from Pearlian causality to the study of image data.

Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.

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.

Automatic term extraction plays an essential role in domain language understanding and several natural language processing downstream tasks. In this paper, we propose a comparative study on the predictive power of Transformers-based pretrained language models toward term extraction in a multi-language cross-domain setting. Besides evaluating the ability of monolingual models to extract single- and multi-word terms, we also experiment with ensembles of mono- and multilingual models by conducting the intersection or union on the term output sets of different language models. Our experiments have been conducted on the ACTER corpus covering four specialized domains (Corruption, Wind energy, Equitation, and Heart failure) and three languages (English, French, and Dutch), and on the RSDO5 Slovenian corpus covering four additional domains (Biomechanics, Chemistry, Veterinary, and Linguistics). The results show that the strategy of employing monolingual models outperforms the state-of-the-art approaches from the related work leveraging multilingual models, regarding all the languages except Dutch and French if the term extraction task excludes the extraction of named entity terms. Furthermore, by combining the outputs of the two best performing models, we achieve significant improvements.

Probabilistic context-free grammars have a long-term record of use as generative models in machine learning and symbolic regression. When used for symbolic regression, they generate algebraic expressions. We define the latter as equivalence classes of strings derived by grammar and address the problem of calculating the probability of deriving a given expression with a given grammar. We show that the problem is undecidable in general. We then present specific grammars for generating linear, polynomial, and rational expressions, where algorithms for calculating the probability of a given expression exist. For those grammars, we design algorithms for calculating the exact probability and efficient approximation with arbitrary precision.

The short-term prediction of precipitation is critical in many areas of life. Recently, a large body of work was devoted to forecasting radar reflectivity images. The radar images are available only in areas with ground weather radars. Thus, we aim to predict high-resolution precipitation from lower-resolution satellite radiance images. A neural network called WeatherFusionNet is employed to predict severe rain up to eight hours in advance. WeatherFusionNet is a U-Net architecture that fuses three different ways to process the satellite data; predicting future satellite frames, extracting rain information from the current frames, and using the input sequence directly. Using the presented method, we achieved 1st place in the NeurIPS 2022 Weather4Cast Core challenge. The code and trained parameters are available at \url{https://github.com/Datalab-FIT-CTU/weather4cast-2022}.