Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.

深度神经网络在数据流是I.I.D的规范环境中的预测和分类任务上表现良好，标记的数据很丰富，并且类标签平衡。随着分配变化的挑战，包括非平稳或不平衡数据流。解决了这一挑战的一种强大方法是在大量未标记的数据上对大型编码器进行自我监督的预处理，然后进行特定于任务的调整。鉴于一项新任务，更新这些编码器的权重是具有挑战性的，因为需要微调大量权重，因此，他们忘记了有关先前任务的信息。在目前的工作中，我们提出了一个模型体系结构来解决此问题，以一个离散的瓶颈为基础，其中包含成对的单独和可学习的（键，价值）代码。在此设置中，我们遵循编码；通过离散瓶颈处理表示形式；和解码范式，其中输入被馈送到预处理的编码器中，编码器的输出用于选择最近的键，并将相应的值馈送到解码器以求解当前任务。该模型只能在推理过程中获取和重复使用有限数量的这些（密钥，值）对，从而启用本地化和上下文依赖的模型更新。从理论上讲，我们研究了所提出的模型最小化分布的影响的能力，并表明与（键，值）配对的这种离散瓶颈降低了假设类别的复杂性。我们经验验证了提出的方法在各种基准数据集的挑战性分配转移方案下的好处，并表明所提出的模型将共同的脆弱性降低到非i.i.d。与其他各种基线相比，非平稳培训分布。

Disonandlement被假设有利于许多下游任务。然而，学习解除不诚位表示的共同假设是数据生成因子在统计上独立。由于目前的方法几乎单独评估在这种理想的假设所在的玩具数据集上，我们在分层设置中调查它们的性能，其现实世界数据的相关特征。在这项工作中，我们介绍了一个具有分层结构的地面实际生成因子的数据集。我们使用这部小型数据集来评估最先进的自动统计文件的解剖模型的性能，并观察到分层模型在分层排列因子的解剖学方面通常优于单层VAE。

Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching the lidar sensor without retraining on a big set of annotated data from the new sensor creates a domain shift, which causes the network performance to drop drastically. In this work we propose a new method for lidar domain adaption, in which we use annotated panoptic lidar datasets and recreate the recorded scenes in the structure of a different lidar sensor. We narrow the domain gap to the target data by recreating panoptic data from one domain in another and mixing the generated data with parts of (pseudo) labeled target domain data. Our method improves the nuScenes to SemanticKITTI unsupervised domain adaptation performance by 15.2 mean Intersection over Union points (mIoU) and by 48.3 mIoU in our semi-supervised approach. We demonstrate a similar improvement for the SemanticKITTI to nuScenes domain adaptation by 21.8 mIoU and 51.5 mIoU, respectively. We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation. Furthermore we successfully apply our proposed method to two entirely unlabeled datasets of two state of the art lidar sensors Velodyne Alpha Prime and InnovizTwo, and train well performing semantic segmentation networks for both.

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures,including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations.

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework facilitating the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.

封闭的量子机械系统的物理学受哈密顿量的约束。但是，在大多数实际情况下，这种哈密顿量尚不清楚，最终所有的数据是从系统上的测量中获得的数据。在这项工作中，我们通过将基于机器学习的基于梯度的优化从机器学习中从张量量的网络中从机器学习中从基于梯度的优化中汇总到从基于梯度的优化的技术中汇总到从动力学数据中进行交互的多体汉密尔顿人来学习的家庭。我们的方法非常实用，实验友好且本质上可扩展，以使系统尺寸超过100次旋转。特别是，我们在综合数据上证明了算法的工作原理，即使仅限于一个简单的初始状态，少量的单量观测和时间演变为相对较短的时间。对于一维海森贝格模型的具体示例，我们的算法在系统大小和缩放的误差常数中作为数据集大小的反平方根。

从不同的随机初始化开始，经过随机梯度下降（SGD）训练的神经网络通常在功能上非常相似，从而提出了一个问题，即不同的SGD溶液之间是否存在有意义的差异。 Entezari等。最近猜想，尽管初始化不同，但在考虑到神经网络的置换不变性后，SGD发现的解决方案位于相同的损失谷中。具体而言，他们假设可以将SGD找到的任何两种解决方案排列，以使其参数之间的线性插值形成一条路径，而不会显着增加损失。在这里，我们使用一种简单但功能强大的算法来找到这样的排列，使我们能够获得直接的经验证据，证明该假设在完全连接的网络中是正确的。引人注目的是，我们发现在初始化时已经存在两个网络，并且平均它们随机，但适当排列的初始化的性能大大高于机会。相反，对于卷积架构，我们的证据表明该假设不存在。特别是在大型学习率制度中，SGD似乎发现了各种模式。

来自原子模拟数据的重建力场（FF）是一个挑战，因为准确的数据可能非常昂贵。在这里，机器学习（ML）模型可以帮助成为数据经济，因为可以使用基础对称性和物理保护定律成功限制它们。但是，到目前为止，每个针对ML模型新提出的描述符都需要进行繁琐且数学繁琐的重塑。因此，我们建议在ML建模过程中使用来自算法分化的现代技术 - 有效地以更高的计算效率的阶顺序自动地使用新颖的描述符或模型。这种范式的方法不仅可以使新的表示形式的多功能用法，对FF社区的有效计算（对FF社区的高价值都高），而且还可以简单地包含进一步的物理知识，例如高阶信息（例如〜Hessians） ，更复杂的部分微分方程约束等），甚至超出了提出的FF域。

我们提出了一个新的图神经网络（GNN）模块，该模块基于最近提出的几何散射变换的松弛，该变换由图形小波滤波器组成。我们可学习的几何散射（腿）模块可以使小波的自适应调整能够鼓励乐队通道特征在学习的表示中出现。与许多流行的GNN相比，我们的腿部模块在GNN中的结合能够学习长期图形关系，这些GNN通常依赖于邻居之间的平滑度或相似性来编码图形结构。此外，与竞争性GNN相比，其小波先验会导致简化的架构，学到的参数明显少得多。我们证明了基于腿的网络在图形分类基准上的预测性能，以及在生化图数据探索任务中学到的功能的描述性质量。我们的结果表明，基于腿部的网络匹配或匹配流行的GNN，以及在许多数据集上，尤其是在生化域中的原始几何散射结构，同时保留了手工制作的（非学习）几何散射的某些数学特性。