多媒体数据集中的异常检测是一个广泛研究的区域。然而，大多数异常检测框架对数据中的概念漂移挑战被忽略或不良处理。最先进的方法假设培训和部署时间的数据分配将相同。但是，由于各种现实生活中的环境因素，数据可能会在其分布中遇到漂移，或者在未来的后期可能会从一个班级漂移。因此，一次经过训练的模型可能无法充分执行。在本文中，我们系统地研究了概念漂移对各种检测模型的影响，并提出了基于修改的自适应高斯混合模型（AGMM）在多媒体数据中用于异常检测的框架。与基线AGMM相反，提议的AGMM延伸时间记得过去更长的时间，以便更好地处理漂移。广泛的实验分析表明，与基线AGMM相比，提出的模型可以更好地处理数据的漂移。此外，为了促进与提议的框架进行研究和比较，我们贡献了三个构成面孔作为样本的多媒体数据集。个体的面部样本对应于十年以上的年龄差异，以纳入更长的时间背景。

Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.

Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model due to better generalizability. To address this challenge, this work proposes Flow, a fine-grained stateless personalized FL approach. Flow creates dynamic personalized models by learning a routing mechanism that determines whether an input instance prefers the local parameters or its global counterpart. Thus, Flow introduces per-instance routing in addition to leveraging per-client personalization to improve accuracies at each client. Further, Flow is stateless which makes it unnecessary for a client to retain its personalized state across FL rounds. This makes Flow practical for large-scale FL settings and friendly to newly joined clients. Evaluations on Stackoverflow, Reddit, and EMNIST datasets demonstrate the superiority in prediction accuracy of Flow over state-of-the-art non-personalized and only per-client personalized approaches to FL.

We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets. Feature duplication arises because DLRM datasets are generated from interactions. While each user session can generate multiple training samples, many features' values do not change across these samples. We demonstrate how RecD exploits this property, end-to-end, across a deployed training pipeline. RecD optimizes data generation pipelines to decrease dataset storage and preprocessing resource demands and to maximize duplication within a training batch. RecD introduces a new tensor format, InverseKeyedJaggedTensors (IKJTs), to deduplicate feature values in each batch. We show how DLRM model architectures can leverage IKJTs to drastically increase training throughput. RecD improves the training and preprocessing throughput and storage efficiency by up to 2.49x, 1.79x, and 3.71x, respectively, in an industry-scale DLRM training system.

Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become impossible for domain scientists to process them manually. Recently, they have started leveraging standard clustering techniques, to reduce the XRD pattern representations requiring manual efforts for labeling and verification. Nevertheless, these standard clustering techniques do not handle problem-specific aspects such as peak shifting, adjacent peaks, background noise, and mixed phases; hence, resulting in incorrect composition-phase diagrams that complicate further steps. Here, we leverage data mining techniques along with domain expertise to handle these issues. In this paper, we introduce an incremental phase mapping approach based on binary peak representations using a new threshold based fuzzy dissimilarity measure. The proposed approach first applies an incremental phase computation algorithm on discrete binary peak representation of XRD samples, followed by hierarchical clustering or manual merging of similar pure phases to obtain the final composition-phase diagram. We evaluate our method on the composition space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are verified by domain scientists and closely resembles the manually computed ground-truth composition-phase diagrams. The proposed approach takes us closer towards achieving the goal of complete end-to-end automated XRD analysis.

当一家企业向另一家企业（B2B）出售时，购买业务由一组称为帐户的个人代表，他们共同决定是否购买。卖方向每个人做广告，并与他们互动，主要是通过数字方式进行的。销售周期很长，通常在几个月内。在寻求信息时，属于帐户的个人之间存在异质性，因此卖方需要在漫长的视野中对每个人的利益进行评分，以决定必须达到哪些人以及何时达到。此外，购买决定与帐户有关，必须进行评分才能投射购买的可能性，这一决定可能会一直变化，直到实际的决定，象征组决策。我们以动态的方式为帐户及其个人的决定分数。动态评分允许机会在长时间的不同时间点影响不同的单个成员。数据集包含与卖方的每个人通信活动的行为日志；但是，没有关于个人之间咨询的数据，这导致了决定。使用神经网络体系结构，我们提出了几种方法来汇总各个成员活动的信息，以预测该小组的集体决策。多次评估发现了强大的模型性能。

以前的无监督句子嵌入研究集中在数据增强方法上，例如辍学和基于规则的句子转换方法。但是，这些方法限制了控制句子增强观点的细粒语义。这导致监督信号不足以捕获类似句子的语义相似性。在这项工作中，我们发现使用邻居句子可以捕获相似句子之间更准确的语义相似性。基于这一发现，我们提出了RankEncoder，该发现使用了输入句子和语料库中的句子之间的关系来训练无监督的句子编码器。我们从三个角度评估rankencoder：1）语义文本相似性性能，2）相似句子对的功效，以及3）rankencoder的普遍性。实验结果表明，与先前的最新性能相比，Rankencoder达到80.07 \％Spearman的相关性，绝​​对提高了1.1％。在类似的句子对上，改进更加显着，改善了1.73％。另外，我们证明了RankEncoder普遍适用于现有的无监督句子编码器。

学习在线推荐模型的关键挑战之一是时间域移动，这会导致培训与测试数据分布之间的不匹配以及域的概括错误。为了克服，我们建议学习一个未来的梯度生成器，该生成器可以预测培训未来数据分配的梯度信息，以便可以对建议模型进行培训，就像我们能够展望其部署的未来一样。与批处理更新相比，我们的理论表明，所提出的算法达到了较小的时间域概括误差，该误差通过梯度变异项在局部遗憾中衡量。我们通过与各种代表性基线进行比较来证明经验优势。

基于机器的最先进的模型是建筑物建模和预测能量行为的流行选择，因为给出了足够的数据，即使在复杂性禁止分析描述的情况下，它们也擅长查找时空模式和结构。但是，基于机器学习的模型用于构建能源预测的模型难以推广到数据中未表示的样本外场景，因为它们的体系结构通常不符合与能源传递现象相关的机械结构的物理对应。因此，他们对看不见的初始条件和边界条件的预测能力完全取决于数据中的代表性，这在构建测量数据中不能保证。因此，这些限制阻碍了它们对现实世界工程应用的应用，例如数字双胞胎的能源管理。作为回应，我们提出了一个域名适应框架，旨在利用对建筑物中能量行为的现象的众所周知的理解，以预测除建筑物测量数据之外的样本场景。更具体地说，我们使用低级别的线性时间不变状态空间模型表示能量行为的机理知识，然后利用其管理结构来预测目标能源系统，仅可用建筑物测量数据。我们通过使在物理衍生的子空间保持一致，该物理衍生的子空间控制全球状态空间行为更接近于测量数据的目标子空间。在最初的探索中，我们专注于线性能源系统。我们通过改变源和目标系统的热物理特性，以证明机械模型从物理学到测量数据的可传递性来测试基于子空间的DA框架。

嵌入学习是深度建议模型中的重要技术，可以将分类特征映射到密集的矢量。但是，嵌入表通常需要大量参数，这些参数成为存储和效率瓶颈。已经采用了分布式培训解决方案将嵌入表分配到多个设备中。但是，如果不仔细分区，则嵌入表很容易导致失衡。这是名为“嵌入桌碎片”的分布式系统的重大设计挑战，即，我们应该如何对嵌入表进行分配以平衡跨设备的成本，这是一项非平凡的任务，因为1）很难有效，精确地衡量成本，和2）已知分区问题是NP-HARD。在这项工作中，我们在Meta中介绍了新颖的实践，即Autoshard，该实践使用神经成本模型直接预测多桌成本和利用深度强化学习以解决分区问题。开源的大规模合成数据集和Meta生产数据集的实验结果证明了Autoshard的优越性优于启发式方法。此外，Autoshard的学习政策可以转移到具有不同数量的表和不同表格比率的碎片任务中，而无需进行任何微调。此外，Autoshard可以在几秒钟内有效地将数百张桌子碎片。 Autoshard的有效性，可转移性和效率使其适合生产使用。我们的算法已在元生产环境中部署。可以在https://github.com/daochenzha/autoshard上获得原型