最近的机器学习趋势一直是通过解释自己的预测的能力来丰富学习模式。到目前为止，迄今为止，可解释的AI（XAI）的新兴领域主要集中在监督学习，特别是深度神经网络分类器。然而，在许多实际问题中，未给出标签信息，并且目标是发现数据的基础结构，例如，其群集。虽然存在强大的方法来提取数据中的群集结构，但它们通常不会回答为什么已分配给给定群集的某些数据点的原因。我们提出了一种新的框架，它首次以有效可靠的方式在输入特征方面解释群集分配。它基于小说洞察力，即聚类模型可以被重写为神经网络 - 或“神经化”。然后，所获得的网络的集群预测可以快速准确地归因于输入特征。几个陈列室展示了我们的方法评估学习集群质量的能力，并从分析的数据和表示中提取新颖的见解。

Accurate modeling of ship performance is crucial for the shipping industry to optimize fuel consumption and subsequently reduce emissions. However, predicting the speed-power relation in real-world conditions remains a challenge. In this study, we used in-service monitoring data from multiple vessels with different hull shapes to compare the accuracy of data-driven machine learning (ML) algorithms to traditional methods for assessing ship performance. Our analysis consists of two main parts: (1) a comparison of sea trial curves with calm-water curves fitted on operational data, and (2) a benchmark of multiple added wave resistance theories with an ML-based approach. Our results showed that a simple neural network outperformed established semi-empirical formulas following first principles. The neural network only required operational data as input, while the traditional methods required extensive ship particulars that are often unavailable. These findings suggest that data-driven algorithms may be more effective for predicting ship performance in practical applications.

Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to interpret the knowledge and reuse it across tasks. Inductive biases can address such issues by explicitly providing generic yet useful decomposition that is otherwise difficult or expensive to learn implicitly. For example, object-centered approaches decompose a high dimensional observation into individual objects. Expanding on this, we utilize an inductive bias for explicit object-centered knowledge separation that provides further decomposition into semantic representations and dynamics knowledge. For this, we introduce a semantic module that predicts an objects' semantic state based on its context. The resulting affordance-like object state can then be used to enrich perceptual object representations. With a minimal setup and an environment that enables puzzle-like tasks, we demonstrate the feasibility and benefits of this approach. Specifically, we compare three different methods of integrating semantic representations into a model-based RL architecture. Our experiments show that the degree of explicitness in knowledge separation correlates with faster learning, better accuracy, better generalization, and better interpretability.

Dexterous manipulation with anthropomorphic robot hands remains a challenging problem in robotics because of the high-dimensional state and action spaces and complex contacts. Nevertheless, skillful closed-loop manipulation is required to enable humanoid robots to operate in unstructured real-world environments. Reinforcement learning (RL) has traditionally imposed enormous interaction data requirements for optimizing such complex control problems. We introduce a new framework that leverages recent advances in GPU-based simulation along with the strength of imitation learning in guiding policy search towards promising behaviors to make RL training feasible in these domains. To this end, we present an immersive virtual reality teleoperation interface designed for interactive human-like manipulation on contact rich tasks and a suite of manipulation environments inspired by tasks of daily living. Finally, we demonstrate the complementary strengths of massively parallel RL and imitation learning, yielding robust and natural behaviors. Videos of trained policies, our source code, and the collected demonstration datasets are available at https://maltemosbach.github.io/interactive_ human_like_manipulation/.

We present Depth-aware Image-based NEural Radiance fields (DINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumetric scene representation that allows us to render 3D objects under novel views. Specifically, we propose novel techniques to incorporate depth information into feature fusion and efficient scene sampling. In comparison to the previous state of the art, DINER achieves higher synthesis quality and can process input views with greater disparity. This allows us to capture scenes more completely without changing capturing hardware requirements and ultimately enables larger viewpoint changes during novel view synthesis. We evaluate our method by synthesizing novel views, both for human heads and for general objects, and observe significantly improved qualitative results and increased perceptual metrics compared to the previous state of the art. The code will be made publicly available for research purposes.

Single-cell reference atlases are large-scale, cell-level maps that capture cellular heterogeneity within an organ using single cell genomics. Given their size and cellular diversity, these atlases serve as high-quality training data for the transfer of cell type labels to new datasets. Such label transfer, however, must be robust to domain shifts in gene expression due to measurement technique, lab specifics and more general batch effects. This requires methods that provide uncertainty estimates on the cell type predictions to ensure correct interpretation. Here, for the first time, we introduce uncertainty quantification methods for cell type classification on single-cell reference atlases. We benchmark four model classes and show that currently used models lack calibration, robustness, and actionable uncertainty scores. Furthermore, we demonstrate how models that quantify uncertainty are better suited to detect unseen cell types in the setting of atlas-level cell type transfer.

我们研究Claire（一种差异性多形状，多-GPU图像注册算法和软件）的性能 - 在具有数十亿素素的大规模生物医学成像应用中。在这样的分辨率下，大多数用于差异图像注册的软件包非常昂贵。结果，从业人员首先要大量删除原始图像，然后使用现有工具进行注册。我们的主要贡献是对降采样对注册性能的影响的广泛分析。我们通过将用Claire获得的全分辨率注册与合成和现实成像数据集的低分辨率注册进行比较，研究了这种影响。我们的结果表明，完全分辨率的注册可以产生卓越的注册质量 - 但并非总是如此。例如，将合成图像从$ 1024^3 $减少到$ 256^3 $将骰子系数从92％降低到79％。但是，对于嘈杂或低对比度的高分辨率图像，差异不太明显。克莱尔不仅允许我们在几秒钟内注册临床相关大小的图像，而且还可以在合理的时间内以前所未有的分辨率注册图像。考虑的最高分辨率是$ 2816 \ times3016 \ times1162 $的清晰图像。据我们所知，这是有关此类决议中图像注册质量的首次研究。

尽管在机器学习的方法论核心中是一个问题，但如何比较分类器仍未达成一致的共识。每个比较框架都面临着（至少）三个基本挑战：质量标准的多样性，数据集的多样性以及选择数据集选择的随机性/任意性。在本文中，我们通过采用决策理论的最新发展，为生动的辩论增添了新的观点。我们最终的框架基于所谓的偏好系统，通过广义的随机优势概念对分类器进行排名，该概念强大地绕过了繁琐的，甚至通常是自相矛盾的，对聚合的依赖。此外，我们表明，可以通过解决易于手柄的线性程序和通过适应的两样本观察随机化测试进行统计测试来实现广泛的随机优势。这确实产生了一个有力的框架，可以同时相对于多个质量标准进行分类器的统计比较。我们在模拟研究和标准基准数据集中说明和研究我们的框架。

与简单英语的德国同行“莱希特·斯普拉奇（Leichte Sprache）”是一种旨在促进复杂的书面语言的受监管语言，否则不同的人群将无法访问。我们为简单德语 - 德语提供了一个新的与句子一致的单语语料库。它包含多个使用自动句子对准方法对齐的文档对准源。我们根据手动标记的对齐文档子集评估我们的对齐方式。通过F1得分衡量的句子对齐质量超过了先前的工作。我们根据CC BY-SA和MIT许可证的随附代码发布数据集。

通过向每个数据示例添加校准的噪声来保护个人的隐私，差异隐私（DP）已成为保护个人隐私的黄金标准。尽管对分类数据的应用很简单，但在图像上下文中的可用性受到限制。与分类数据相反，图像的含义是相邻像素的空间相关性固有的，使噪声的简单应用不可行。可逆的神经网络（INN）表现出了出色的生成性能，同时仍提供量化确切可能性的能力。他们的原理是基于将复杂的分布转换为一个简单的分布，例如图像进入球形高斯。我们假设在旅馆的潜在空间中添加噪音可以实现差异化的私有图像修改。操纵潜在空间会导致修改的图像，同时保留重要的细节。此外，通过对数据集提供的元数据进行调节，我们旨在使对下游任务的尺寸保持重要意义，例如分类未触及的，同时更改其他可能包含识别信息的其他部分。我们称我们的方法意识到差异隐私（CADP）。我们对公共基准测试数据集以及专用医疗进行实验。此外，我们还展示了方法对分类数据的普遍性。源代码可在https://github.com/cardio-ai/cadp上公开获得。