We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features. We follow the strategy of implicit distribution modelling via generative adversarial network (GAN) combined with permutation equivariant message passing architecture operating over the sets of nodes and edges. This enables generating the feature vectors of all the graph objects in one go (in 2 phases) as opposed to a much slower one-by-one generations of sequential models, prevents the need for expensive graph matching procedures usually needed for likelihood-based generative models, and uses efficiently the network capacity by being insensitive to the particular node ordering in the graph representation. To the best of our knowledge, this is the first method that models the feature distribution along the graph skeleton allowing for generations of annotated graphs with user specified structures. Our experiments demonstrate the ability of our model to learn complex structured distributions through quantitative evaluation over three annotated graph datasets.

图形生成建模中讨论的最多的一个问题之一是表示的排序。一个解决方案包括使用等分性的生成功能，确保排序不变性。在讨论了这种功能的一些性质之后，我们提出了3G-GaN，这是一个依赖于GAN和等价函数的3级模型。该模型仍在开发中。但是，我们展示了一些鼓励探索性实验，并讨论仍有待解决的问题。

For an autonomous vehicle, the ability to sense its surroundings and to build an overall representation of the environment by fusing different sensor data streams is fundamental. To this end, the poses of all sensors need to be accurately determined. Traditional calibration methods are based on: 1) using targets specifically designed for calibration purposes in controlled environments, 2) optimizing a quality metric of the point clouds collected while traversing an unknown but static environment, or 3) optimizing the match among per-sensor incremental motion observations along a motion path fulfilling special requirements. In real scenarios, however, the online applicability of these methods can be limited, as they are typically highly dynamic, contain degenerate paths, and require fast computations. In this paper, we propose an approach that tackles some of these challenges by formulating the calibration problem as a joint but structured optimization problem of all sensor calibrations that takes as input a summary of the point cloud information consisting of ground points and pole detections. We demonstrate the efficiency and quality of the results of the proposed approach in a set of experiments with LiDAR simulation and real data from an urban trip.

Sigmorphon 2022关于词素分割的共享任务挑战了将单词分解为一系列词素的系统，并涵盖了大多数类型的形态：化合物，衍生和弯曲。子任务1，单词级词素细分，涵盖了9种语言的500万个单词（捷克，英语，西班牙语，匈牙利语，法语，意大利语，俄语，拉丁语，蒙古语），并收到了7个团队的13个系统提交，最佳系统平均为97.29％F1在所有语言中得分，英语（93.84％）到拉丁语（99.38％）。子任务2，句子级的词素细分，涵盖了3种语言的18,735个句子（捷克，英语，蒙古人），从3个团队中收到10个系统提交，最好的系统优于所有三种最先进的子字体化方法（BPE（BPE），Ulm，Morfessor2）绝对30.71％。为了促进错误分析并支持任何类型的未来研究，我们发布了所有系统预测，评估脚本和所有黄金标准数据集。