许多现代的在线3D应用程序和视频游戏都依靠人脸的参数模型来创建可信的化身。但是，用参数模型手动复制某人的面部相似性是困难且耗时的。该任务的机器学习解决方案是非常可取的，但也充满挑战。本文提出了一种新的方法来解决所谓的面对参数问题（简称F2P），旨在重建单个图像的参数面。所提出的方法利用合成数据，域分解和域适应来解决解决F2P的多方面挑战。开源代码库说明了我们的主要观察结果，并提供了定量评估的手段。提出的方法在工业应用中证明是实际的。它提高了准确性并允许更有效的模型培训。这些技术有可能扩展到其他类型的参数模型。

许多现代的在线3D应用程序和视频游戏依靠人面孔的参数模型来创建可信的化身。但是，使用参数模型对某人的面部相似性进行手动复制是困难且耗时的。该任务的机器学习解决方案是非常可取的，但也充满挑战。本文提出了一种新的方法来解决所谓的面对参数问题（简称F2P），旨在重建单个图像的参数面。所提出的方法利用合成数据，域分解和域适应来解决解决F2P的多方面挑战。开源代码库说明了我们的主要观察结果，并提供了定量评估的手段。提出的方法在工业应用中证明是实际的。它提高了准确性并允许更有效的模型培训。这些技术有可能扩展到其他类型的参数模型。

Deep reinforcement learning has considerable potential to improve irrigation scheduling in many cropping systems by applying adaptive amounts of water based on various measurements over time. The goal is to discover an intelligent decision rule that processes information available to growers and prescribes sensible irrigation amounts for the time steps considered. Due to the technical novelty, however, the research on the technique remains sparse and impractical. To accelerate the progress, the paper proposes a general framework and actionable procedure that allow researchers to formulate their own optimisation problems and implement solution algorithms based on deep reinforcement learning. The effectiveness of the framework was demonstrated using a case study of irrigated wheat grown in a productive region of Australia where profits were maximised. Specifically, the decision rule takes nine state variable inputs: crop phenological stage, leaf area index, extractable soil water for each of the five top layers, cumulative rainfall and cumulative irrigation. It returns a probabilistic prescription over five candidate irrigation amounts (0, 10, 20, 30 and 40 mm) every day. The production system was simulated at Goondiwindi using the APSIM-Wheat crop model. After training in the learning environment using 1981--2010 weather data, the learned decision rule was tested individually for each year of 2011--2020. The results were compared against the benchmark profits obtained using irrigation schedules optimised individually for each of the considered years. The discovered decision rule prescribed daily irrigation amounts that achieved more than 96% of the benchmark profits. The framework is general and applicable to a wide range of cropping systems with realistic optimisation problems.

We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.

Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors. The Spatio-temporal feature in traffic data brings more challenges for processing such missing values, for which the classic techniques (e.g., data imputations) are limited: 1) in temporal axis, the values can be randomly or consecutively missing; 2) in spatial axis, the missing values can happen on one single sensor or on multiple sensors simultaneously. Recent models powered by Graph Neural Networks achieved satisfying performance on traffic forecasting tasks. However, few of them are applicable to such a complex missing-value context. To this end, we propose GCN-M, a Graph Convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context. Particularly, we jointly model the missing value processing and traffic forecasting tasks, considering both local Spatio-temporal features and global historical patterns in an attention-based memory network. We propose as well a dynamic graph learning module based on the learned local-global features. The experimental results on real-life datasets show the reliability of our proposed method.

在本文中，我们介绍Bayesldm，这是一个用于贝叶斯纵向数据建模的系统，该系统由高级建模语言组成，具有针对复杂的多变量时间序列数据建模的特定功能，并与编译器相结合，可以生成优化的概率程序代码，以在指定模型中执行指定的推理。 Bayesldm支持贝叶斯网络模型的建模，其特定关注动态贝叶斯网络（DBN）的高效，声明性规范。 Bayesldm编译器将模型规范与可用数据和输出代码相结合，用于执行贝叶斯推断，以同时处理丢失的数据，同时处理未知模型参数。这些功能有可能通过抽象产生计算有效的概率推断代码的过程来显着加速域中的迭代建模工作流，这些迭代建模工作流程涉及复杂纵向数据的分析。我们描述了Bayesldm系统组件，评估表示和推理优化的效率，并提供了该系统在分析异质和部分观察到的移动健康数据的应用示例。