当代企业在以不确定性,敌意和纯粹的数据量为特征的情况下广泛应用机器推理和人工智能提供了无限的机会。该论文开发了一个评估网络,作为在支持人类运营商的不确定性下进行高级融合和推理的图形系统。估值是(不确定的)知识和收集数据的数学表示形式,被表示为信用集,定义为不精确概率理论框架中的连贯间隔概率。具有这种信用集,组合和边缘化的基本操作被定义为满足评估代数的公理。讨论了信用估值网络的实际实施,并在一个小规模的示例中证明了其实用性。
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高斯进程(GPS)是通过工程学的社会和自然科学的应用程序学习和统计数据的重要工具。它们构成具有良好校准的不确定性估计的强大的内核非参数方法,然而,由于其立方计算复杂度,从货架上的GP推理程序仅限于具有数千个数据点的数据集。因此,在过去几年中已经开发出许多稀疏的GPS技术。在本文中,我们专注于GP回归任务,并提出了一种基于来自几个本地和相关专家的聚合预测的新方法。因此,专家之间的相关程度可以在独立于完全相关的专家之间变化。考虑到他们的相关性导致了一致的不确定性估算,汇总了专家的个人预测。我们的方法在限制案件中恢复了专家的独立产品,稀疏GP和全GP。呈现的框架可以处理一般的内核函数和多个变量,并且具有时间和空间复杂性,在专家和数据样本的数量中是线性的,这使得我们的方法是高度可扩展的。我们展示了我们提出的方法的卓越性能,这是我们提出的综合性和几个实际数据集的最先进的GP近似方法的卓越性能,以及具有确定性和随机优化的若干现实世界数据集。
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Bayesian Optimization(BO)是全球优化的黑匣子客观功能的方法,这是昂贵的评估。 Bo Powered实验设计在材料科学,化学,实验物理,药物开发等方面发现了广泛的应用。这项工作旨在提请注意应用BO在设计实验中的益处,并提供博手册,涵盖方法和软件,为了方便任何想要申请或学习博的人。特别是,我们简要解释了BO技术,审查BO中的所有应用程序在添加剂制造中,比较和举例说明不同开放BO库的功能,解锁BO的新潜在应用,以外的数据(例如,优先输出)。本文针对读者,了解贝叶斯方法的一些理解,但不一定符合添加剂制造的知识;软件性能概述和实施说明是任何实验设计从业者的乐器。此外,我们在添加剂制造领域的审查突出了博的目前的知识和技术趋势。本文在线拥有补充材料。
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Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.
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Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring mechanisms have been proposed as preprocessing steps. Graph Echo State Networks (GESNs) are a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. In this paper, we show that GESNs can achieve a significantly better accuracy on six heterophilic node classification tasks without altering the graph connectivity, thus suggesting a different route for addressing the over-squashing problem.
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This paper presents the development of a system able to estimate the 2D relative position of nodes in a wireless network, based on distance measurements between the nodes. The system uses ultra wide band ranging technology and the Bluetooth Low Energy protocol to acquire data. Furthermore, a nonlinear least squares problem is formulated and solved numerically for estimating the relative positions of the nodes. The localization performance of the system is validated by experimental tests, demonstrating the capability of measuring the relative position of a network comprised of 4 nodes with an accuracy of the order of 3 cm and an update rate of 10 Hz. This shows the feasibility of applying the proposed system for multi-robot cooperative localization and formation control scenarios.
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Recent 3D-aware GANs rely on volumetric rendering techniques to disentangle the pose and appearance of objects, de facto generating entire 3D volumes rather than single-view 2D images from a latent code. Complex image editing tasks can be performed in standard 2D-based GANs (e.g., StyleGAN models) as manipulation of latent dimensions. However, to the best of our knowledge, similar properties have only been partially explored for 3D-aware GAN models. This work aims to fill this gap by showing the limitations of existing methods and proposing LatentSwap3D, a model-agnostic approach designed to enable attribute editing in the latent space of pre-trained 3D-aware GANs. We first identify the most relevant dimensions in the latent space of the model controlling the targeted attribute by relying on the feature importance ranking of a random forest classifier. Then, to apply the transformation, we swap the top-K most relevant latent dimensions of the image being edited with an image exhibiting the desired attribute. Despite its simplicity, LatentSwap3D provides remarkable semantic edits in a disentangled manner and outperforms alternative approaches both qualitatively and quantitatively. We demonstrate our semantic edit approach on various 3D-aware generative models such as pi-GAN, GIRAFFE, StyleSDF, MVCGAN, EG3D and VolumeGAN, and on diverse datasets, such as FFHQ, AFHQ, Cats, MetFaces, and CompCars. The project page can be found: \url{https://enisimsar.github.io/latentswap3d/}.
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This paper studies the infinite-width limit of deep linear neural networks initialized with random parameters. We obtain that, when the number of neurons diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a gradient descent on an infinitely wide deterministic linear neural network. Moreover, even if the weights remain random, we get their precise law along the training dynamics, and prove a quantitative convergence result of the linear predictor in terms of the number of neurons. We finally study the continuous-time limit obtained for infinitely wide linear neural networks and show that the linear predictors of the neural network converge at an exponential rate to the minimal $\ell_2$-norm minimizer of the risk.
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We investigate the sample complexity of learning the optimal arm for multi-task bandit problems. Arms consist of two components: one that is shared across tasks (that we call representation) and one that is task-specific (that we call predictor). The objective is to learn the optimal (representation, predictor)-pair for each task, under the assumption that the optimal representation is common to all tasks. Within this framework, efficient learning algorithms should transfer knowledge across tasks. We consider the best-arm identification problem for a fixed confidence, where, in each round, the learner actively selects both a task, and an arm, and observes the corresponding reward. We derive instance-specific sample complexity lower bounds satisfied by any $(\delta_G,\delta_H)$-PAC algorithm (such an algorithm identifies the best representation with probability at least $1-\delta_G$, and the best predictor for a task with probability at least $1-\delta_H$). We devise an algorithm OSRL-SC whose sample complexity approaches the lower bound, and scales at most as $H(G\log(1/\delta_G)+ X\log(1/\delta_H))$, with $X,G,H$ being, respectively, the number of tasks, representations and predictors. By comparison, this scaling is significantly better than the classical best-arm identification algorithm that scales as $HGX\log(1/\delta)$.
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In recent years, there has been a growing interest in the effects of data poisoning attacks on data-driven control methods. Poisoning attacks are well-known to the Machine Learning community, which, however, make use of assumptions, such as cross-sample independence, that in general do not hold for linear dynamical systems. Consequently, these systems require different attack and detection methods than those developed for supervised learning problems in the i.i.d.\ setting. Since most data-driven control algorithms make use of the least-squares estimator, we study how poisoning impacts the least-squares estimate through the lens of statistical testing, and question in what way data poisoning attacks can be detected. We establish under which conditions the set of models compatible with the data includes the true model of the system, and we analyze different poisoning strategies for the attacker. On the basis of the arguments hereby presented, we propose a stealthy data poisoning attack on the least-squares estimator that can escape classical statistical tests, and conclude by showing the efficiency of the proposed attack.
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