我们介绍MedCod，一种医学准确，情感，多样化和可控的对话系统，具有独特的自然语言发生器模块的方法。 MedCod已经开发并专门为历史为任务进行了评估。它集成了传统模块化方法的优势，使（医学）域知识与现代深层学习技术结合起来，以产生灵活的人类自然语言表达。详细描述了Medcod的自然语言输出的两个关键方面。首先，生成的句子是情绪化的，同样地看着医生如何与患者沟通。其次，生成的句子结构和措辞是多样化的，同时保持与所需医学概念的医疗一致性（由Medcod的对话管理器模块提供）。实验结果表明了我们在创造人类医疗对话系统方面的有效性。相关代码在https://github.com/curai/curai-research/tree/main/medcod提供

在患者和医生之间的相互作用期间收集的捕获信息中，医疗谈话摘要是一体的。总结谈话用于促进医生之间的患者交出，以及将来提供护理的一部分。然而，摘要可能是生产和需要域专业知识的耗时。现代培训的培训NLP型号，如Pegasus已经成为人类总结的有能力的替代方案，在许多摘要基准上达到最先进的性能。然而，许多下游任务仍然需要至少适度尺寸的数据集来实现令人满意的性能。在这项工作中，我们（1）探讨了数据集大小对使用Pegasus的转移学习医疗会话摘要的影响，（2）在分类设置中取得成功之后，评估低数据制度的各种迭代标记策略。我们发现模型性能随着数据集大小的增加而饱和，并且各种主动学习策略评估所有显示与简单数据集大小的等效性能一致。我们还发现天真的迭代伪标签是比没有伪标签的典型或略差。我们的工作阐明了将低数据制度技术转化为医学谈话摘要的概率和挑战，帮助指导未来在这个空间中的工作。可用的相关代码在\ url {https://github.com/curai/curai-research/tree/main/medical-summarization-ml4h-2021}。

Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users with similar movie preferences, where the users may still rank the individual movies slightly differently. Such tasks can be organized in a hierarchy, where similar tasks are related through a shared structure. In this work, we formulate this problem as a contextual off-policy optimization in a hierarchical graphical model from logged bandit feedback. To solve the problem, we propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We instantiate HierOPO in linear Gaussian models, for which we also provide an efficient implementation and analysis. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. We also evaluate the policies empirically. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.

Path prediction is an essential task for many real-world Cyber-Physical Systems (CPS) applications, from autonomous driving and traffic monitoring/management to pedestrian/worker safety. These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for multiple subjects (e.g., pedestrians and vehicles) from different perspectives. However, most existing algorithms are tailor-made for a unique subject with a specific camera perspective and scenario. This article presents Pishgu, a universal lightweight network architecture, as a robust and holistic solution for path prediction. Pishgu's architecture can adapt to multiple path prediction domains with different subjects (vehicles, pedestrians), perspectives (bird's-eye, high-angle), and scenes (sidewalk, highway). Our proposed architecture captures the inter-dependencies within the subjects in each frame by taking advantage of Graph Isomorphism Networks and the attention module. We separately train and evaluate the efficacy of our architecture on three different CPS domains across multiple perspectives (vehicle bird's-eye view, pedestrian bird's-eye view, and human high-angle view). Pishgu outperforms state-of-the-art solutions in the vehicle bird's-eye view domain by 42% and 61% and pedestrian high-angle view domain by 23% and 22% in terms of ADE and FDE, respectively. Additionally, we analyze the domain-specific details for various datasets to understand their effect on path prediction and model interpretation. Finally, we report the latency and throughput for all three domains on multiple embedded platforms showcasing the robustness and adaptability of Pishgu for real-world integration into CPS applications.

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.

图形神经网络（GNN）已成为编码图形结构数据的强大工具。由于其广泛的应用程序，越来越需要开发工具来解释GNN如何做出给定的图形结构数据决定。现有的基于学习的GNN解释方法在培训中是特定于任务的，因此遭受了关键的缺点。具体而言，它们无法为使用单个解释器提供多任务预测模型的解释。在GNN以自我监督的方式训练的情况下，他们也无法提供解释，并且在未来的下游任务中使用了结果表示。为了解决这些局限性，我们提出了一个任务不合时宜的GNN解释器（TAGE），该解释器（Tage）独立于下游模型，并在自学人员的情况下接受了训练，而对下游任务不了解。 Tage可以通过看不见的下游任务来解释GNN嵌入模型，并可以有效解释多任务模型。我们的广泛实验表明，通过使用相同的模型来解释多个下游任务的预测，同时实现了与当前最新的GNN解释方法一样好甚至更好的解释质量，可以显着提高解释效率。我们的代码可公开作为DIG库的一部分，网址为https://github.com/divelab/dig/tree/main/main/dig/xgraph/tage/。

图形神经网络（GNNS）在节点分类，回归和推荐任务中取得了最新的最新性能。当可提供高质量和丰富的连接结构时，GNNS工作好。但是，在许多真实世界图中，该要求在节点度具有幂律分布的许多真实世界中，因为许多节点具有较少或嘈杂的连接。这种情况的极端情况是节点可能没有邻居，称为严格的冷启动（SCS）场景。这会强制预测模型依赖于节点的输入特征。与通过蒸馏方法相比，我们提出冷啤酒以解决SCS和嘈杂的邻居设置。我们介绍了功能贡献比（FCR），测量使用电感GNN解决SCS问题的可行性，并选择SCS泛化的最佳体系结构。我们通过实验显示FCR Disentangles图数据集的各种组成部分的贡献，并展示了几个公共基准和专有电子商务数据集上的冷啤酒的优越性。我们方法的源代码可用于：https://github.com/amazon-research/gnn-tail-一致化。