Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery has become an important operation in data lake management. Existing approaches target equi-joins, the most common way of combining tables for creating a unified view, or semantic joins, which tolerate misspellings and different formats to deliver more join results. They are either exact solutions whose running time is linear in the sizes of query column and target table repository or approximate solutions lacking precision. In this paper, we propose Deepjoin, a deep learning model for accurate and efficient joinable table discovery. Our solution is an embedding-based retrieval, which employs a pre-trained language model (PLM) and is designed as one framework serving both equi- and semantic joins. We propose a set of contextualization options to transform column contents to a text sequence. The PLM reads the sequence and is fine-tuned to embed columns to vectors such that columns are expected to be joinable if they are close to each other in the vector space. Since the output of the PLM is fixed in length, the subsequent search procedure becomes independent of the column size. With a state-of-the-art approximate nearest neighbor search algorithm, the search time is logarithmic in the repository size. To train the model, we devise the techniques for preparing training data as well as data augmentation. The experiments on real datasets demonstrate that by training on a small subset of a corpus, Deepjoin generalizes to large datasets and its precision consistently outperforms other approximate solutions'. Deepjoin is even more accurate than an exact solution to semantic joins when evaluated with labels from experts. Moreover, when equipped with a GPU, Deepjoin is up to two orders of magnitude faster than existing solutions.

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.

大多数图形神经网络（GNN）通过学习输入图和标签之间的相关性来预测看不见的图的标签。但是，通过对具有严重偏见的训练图进行图形分类调查，我们发现GNN始终倾向于探索伪造的相关性以做出决定，即使因果关系始终存在。这意味着在此类偏见的数据集中接受培训的现有GNN将遭受概括能力差。通过在因果观点中分析此问题，我们发现从偏见图中解开和去偏置因果和偏见的潜在变量对于偏见至关重要。在此鼓舞下，我们提出了一个普遍的分解GNN框架，分别学习因果子结构和偏见子结构。特别是，我们设计了一个参数化的边蒙版生成器，以将输入图明确分为因果和偏置子图。然后，分别由因果/偏见感知损失函数监督的两个GNN模块进行培训，以编码因果关系和偏置子图表中的相应表示。通过分离的表示，我们合成了反事实无偏的训练样本，以进一步脱离因果变量和偏见变量。此外，为了更好地基于严重的偏见问题，我们构建了三个新的图形数据集，这些数据集具有可控的偏置度，并且更容易可视化和解释。实验结果很好地表明，我们的方法比现有基线实现了优越的概括性能。此外，由于学习的边缘面膜，该拟议的模型具有吸引人的解释性和可转让性。代码和数据可在以下网址获得：https：//github.com/googlebaba/disc。

联合学习是一种分布式的机器学习方法，其中单个服务器和多个客户端在不共享客户端数据集的情况下协作构建机器学习模型。联合学习的一个具有挑战性的问题是数据异质性（即，数据分布在客户端可能有所不同）。为了应对这个问题，众多联合学习方法旨在为客户提供个性化的联合学习，并为客户建立优化的模型。尽管现有研究通过经验评估了自己的方法，但这些研究中的实验环境（例如比较方法，数据集和客户设置）彼此不同，目前尚不清楚哪种个性化的联邦学习方法可以实现最佳性能，以及取得多少进展，可以进行多大进展。通过使用这些方法而不是标准（即非个人化）联合学习来制作。在本文中，我们通过全面的实验基准了现有的个性化联合学习的性能，以评估每种方法的特征。我们的实验研究表明，（1）没有冠军方法，（2）大数据异质性通常会导致高准确的预测，并且（3）具有微调的标准联合学习方法（例如FedAvg）通常超过了个性化的联邦学习方法。我们为研究人员开放基准工具FedBench，以通过各种实验环境进行实验研究。

开发用于训练图形的可扩展解决方案，用于链路预测任务的Neural网络（GNNS）由于具有高计算成本和巨大内存占用的高数据依赖性，因此由于高数据依赖性而具有挑战性。我们提出了一种新的方法，用于缩放知识图形嵌入模型的培训，以满足这些挑战。为此，我们提出了以下算法策略：自给自足的分区，基于约束的负采样和边缘迷你批量培训。两者都是分区策略和基于约束的负面采样，避免在训练期间交叉分区数据传输。在我们的实验评估中，我们表明，我们基于GNN的知识图形嵌入模型的缩放解决方案在基准数据集中实现了16倍的加速，同时将可比的模型性能作为标准度量的非分布式方法。

建议图表神经网络（GNNS）在不考虑训练和测试图之间的不可知分布的情况下，诱导GNN的泛化能力退化在分布外（OOD）设置。这种退化的根本原因是大多数GNN是基于I.I.D假设开发的。在这种设置中，GNN倾向于利用在培训中存在的微妙统计相关性用于预测，即使它是杂散的相关性。然而，这种杂散的相关性可能在测试环境中改变，导致GNN的失败。因此，消除了杂散相关的影响对于稳定的GNN来说是至关重要的。为此，我们提出了一个普遍的因果代表框架，称为稳定凝球。主要思想是首先从图数据中提取高级表示，并诉诸因因果推理的显着能力，以帮助模型摆脱虚假相关性。特别是，我们利用图形池化层以提取基于子图的表示作为高级表示。此外，我们提出了一种因果变量区别，以纠正偏置训练分布。因此，GNN将更多地集中在稳定的相关性上。对合成和现实世界ood图数据集的广泛实验良好地验证了所提出的框架的有效性，灵活性和可解释性。

尽管图形神经网络（GNNS）已经取得了显着的准确性，但结果是否值得信赖仍未开发。以前的研究表明，许多现代神经网络对预测过度充满信心，然而，令人惊讶的是，我们发现GNN主要呈相反方向，即，GNN是不受自信的。因此，非常需要GNN的置信度校准。在本文中，我们通过设计拓扑知识的后HOC校准函数提出了一种新型值得信赖的GNN模型。具体而言，我们首先验证图形中的置信度分布具有同眼性的财产，而且这一发现激发了我们设计校准GNN模型（CAGCN）以学习校准功能。 CAGCN能够从GNN的Logits对每个节点的校准置信度获得独特的变换，同时，这种变换能够在类之间保留课程之间的顺序，满足精度保留的属性。此外，我们将校准GNN应用于自培训框架，表明可以通过校准的置信度获得更可靠的伪标签，并进一步提高性能。广泛的实验证明了我们所提出的模型在校准和准确性方面的有效性。

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.