We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corresponding point in the source image directly along the corresponding epipolar line. We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo. Previous works in deep MVS select a range of interest within the depth space, discretize it, and sample the epipolar line according to the resulting depth values: this can result in an uneven scanning of the epipolar line, hence of the image space. Instead, our method works directly on the epipolar line: this guarantees an even scanning of the image space and avoids both the need to select a depth range of interest, which is often not known a priori and can vary dramatically from scene to scene, and the need for a suitable discretization of the depth space. In fact, our search is iterative, which avoids the building of a cost volume, costly both to store and to process. Finally, our method performs a robust geometry-aware fusion of the estimated depth maps, leveraging a confidence predicted alongside each depth. We test DELS-MVS on the ETH3D, Tanks and Temples and DTU benchmarks and achieve competitive results with respect to state-of-the-art approaches.

在许多计算机视觉管道中，在图像之间建立一组稀疏的关键点相关性是一项基本任务。通常，这转化为一个计算昂贵的最近邻居搜索，必须将一个图像的每个键盘描述符与其他图像的所有描述符进行比较。为了降低匹配阶段的计算成本，我们提出了一个能够检测到每个图像处的互补关键集的深度提取网络。由于仅需要在不同图像上比较同一组中的描述符，因此匹配相计算复杂度随集合数量而降低。我们训练我们的网络以预测关键点并共同计算相应的描述符。特别是，为了学习互补的关键点集，我们引入了一种新颖的无监督损失，对不同集合之间的交叉点进行了惩罚。此外，我们提出了一种基于描述符的新型加权方案，旨在惩罚使用非歧视性描述符的关键点的检测。通过广泛的实验，我们表明，我们的功能提取网络仅在合成的扭曲图像和完全无监督的方式进行训练，以降低匹配的复杂性，在3D重建和重新定位任务上取得了竞争成果。

可以将一组个人或组织之间的战略互动建模为在网络上玩的游戏，在网络上，玩家的回报不仅取决于他们的行动，还取决于邻居的行动。从观察到的游戏结果（平衡动作）中推断网络结构是一个重要的问题，对于经济学和社会科学中的许多潜在应用。现有方法主要需要与游戏相关的效用函数的知识，在现实世界中，这通常是不现实的。我们采用类似变压器的体系结构，该体系结构正确说明了问题的对称性，并在没有明确了解效用功能的情况下学习了从平衡动作到游戏网络结构的映射。我们使用合成和现实世界数据在三种不同类型的网络游戏上测试我们的方法，并证明其在网络结构推理中的有效性和优于现有方法的卓越性能。

许多现代神经架构的核心的卷积运算符可以有效地被视为在输入矩阵和滤波器之间执行点产品。虽然这很容易适用于诸如图像的数据，其可以在欧几里德空间中表示为常规网格，延伸卷积操作者以在图形上工作，而是由于它们的不规则结构而被证明更具有挑战性。在本文中，我们建议使用图形内部产品的图形内核，即在图形上计算内部产品，以将标准卷积运算符扩展到图形域。这使我们能够定义不需要计算输入图的嵌入的完全结构模型。我们的架构允许插入任何类型和数量的图形内核，并具有在培训过程中学到的结构面具方面提供一些可解释性的额外益处，类似于传统卷积神经网络中的卷积掩模发生的事情。我们执行广泛的消融研究，调查模型超参数的影响，我们表明我们的模型在标准图形分类数据集中实现了竞争性能。

虽然图形神经网络（GNNS）最近成为用于建模关系数据的事实标准，但它们对图形节点或边缘特征的可用性产生了强烈的假设。然而，在许多现实世界应用中，功能仅部分可用;例如，在社交网络中，年龄和性别仅适用于一小部分用户。我们介绍了一种用于处理基于Dirichlet能量最小化的图形机学习应用中缺失特征的一般方法，并导致图表上的扩散型微分方程。该等方程的离散化产生了一种简单，快速且可伸缩的算法，我们调用特征传播。我们通过实验表明，所提出的方法在七个常见节点分类基准测试中优于先前的方法，并且可以承受令人惊讶的缺失特点率：平均而言，当缺少99％的功能时，我们只观察到约4％的相对精度下降。此外，在单个GPU上运行$ \ SIM $ 2.5M节点和$ \ SIM $ 123M边缘，只需10秒即可在单个GPU上运行。

According to the latest trend of artificial intelligence, AI-systems needs to clarify regarding general,specific decisions,services provided by it. Only consumer is satisfied, with explanation , for example, why any classification result is the outcome of any given time. This actually motivates us using explainable or human understandable AI for a behavioral mining scenario, where users engagement on digital platform is determined from context, such as emotion, activity, weather, etc. However, the output of AI-system is not always systematically correct, and often systematically correct, but apparently not-perfect and thereby creating confusions, such as, why the decision is given? What is the reason underneath? In this context, we first formulate the behavioral mining problem in deep convolutional neural network architecture. Eventually, we apply a recursive neural network due to the presence of time-series data from users physiological and environmental sensor-readings. Once the model is developed, explanations are presented with the advent of XAI models in front of users. This critical step involves extensive trial with users preference on explanations over conventional AI, judgement of credibility of explanation.

Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

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.