最近提出的神经网络的规模不断增加，因此很难在嵌入式设备上实现它们，在嵌入式设备上，内存，电池和计算功率是一种非平凡的瓶颈。因此，在过去几年中，网络压缩文献一直在蓬勃发展，并且已经发布了大量解决方案，以减少模型的操作数量和参数。不幸的是，大多数这些还原技术实际上是启发式方法，通常需要至少一个重新训练的步骤才能恢复准确性。在验证和性能评估领域中，对模型降低的程序的需求也众所周知，在这些领域中，大量努力致力于保留可观察到的潜在行为的商的定义。在本文中，我们试图弥合最流行和非常有效的网络减少策略与正式概念（例如块状性）之间的差距，以验证和评估马尔可夫链。详细阐述肿块，我们提出了一种修剪方法，该方法可以减少网络中的神经元数，而无需使用任何数据或微调，同时完全保留了确切的行为。放松对商方法的确切定义的限制，我们可以对一些最常见的还原技术进行形式解释。

我们介绍了革兰氏 - 哈达马德密度运算符（GHDO），这是一种新的深神经网络结构，可以用多项式资源编码指数级的正差半准密度运算符。然后，我们展示如何在GHDO中嵌入自回归结构，以直接对概率分布进行采样。当表示与环境相互作用的系统的混合量子状态时，这些属性尤为重要。最后，我们通过模拟耗散横向场模型的稳态来对此结构进行基准测试。估计局部可观察物和r \'enyi熵，我们对先前最新的变异方法显示出显着改善。

我们证明，任何矩阵产品状态（MP）可以通过线性内存更新的复发神经网络（RNN）来精确表示。我们使用多线性内存更新将此RNN体系结构推广到2D晶格。它支持在多项式时间内的完美采样和波功能评估，并且可以代表纠缠熵的区域定律。数值证据表明，与MPS相比，它可以使用键尺寸较低的键尺寸编码波函数，其精度可以通过增加键尺寸来系统地改善。

复杂的高尺寸概率分布的高效采样是计算科学中的核心任务。机器学习方法，如自动增加神经网络，与马尔可夫链蒙特卡罗采样一起使用，为这种分布提供良好的近似，但遭受内在偏差或高方差。在这封信中，我们提出了一种方法来使这种近似不偏不倚，方差低。我们的方法使用物理对称和可变大小的群集更新，它利用自回归分解的结构。我们测试我们的古典自旋系统的第一阶和二阶相变的方法，显示其对关键系统和亚稳态存在的可行性。

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.

G-Enum histograms are a new fast and fully automated method for irregular histogram construction. By framing histogram construction as a density estimation problem and its automation as a model selection task, these histograms leverage the Minimum Description Length principle (MDL) to derive two different model selection criteria. Several proven theoretical results about these criteria give insights about their asymptotic behavior and are used to speed up their optimisation. These insights, combined to a greedy search heuristic, are used to construct histograms in linearithmic time rather than the polynomial time incurred by previous works. The capabilities of the proposed MDL density estimation method are illustrated with reference to other fully automated methods in the literature, both on synthetic and large real-world data sets.