In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously streamed over time, for both learning and inference. Existing libraries are designed to exploit data-level parallelism, assuming that samples are batched, a condition that is not naturally met in applications that are based on streamed data. Differently, PARTIME starts processing each data sample at the time in which it becomes available from the stream. PARTIME wraps the code that implements a feed-forward multi-layer network and it distributes the layer-wise processing among multiple devices, such as Graphics Processing Units (GPUs). Thanks to its pipeline-based computational scheme, PARTIME allows the devices to perform computations in parallel. At inference time this results in scaling capabilities that are theoretically linear with respect to the number of devices. During the learning stage, PARTIME can leverage the non-i.i.d. nature of the streamed data with samples that are smoothly evolving over time for efficient gradient computations. Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning, distributing operations on up to 8 NVIDIA GPUs, showing significant speedups that are almost linear in the number of devices, mitigating the impact of the data transfer overhead.
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部署AI驱动的系统需要支持有效人类互动的值得信赖的模型,超出了原始预测准确性。概念瓶颈模型通过在类似人类的概念的中间级别调节分类任务来促进可信度。这使得人类干预措施可以纠正错误预测的概念以改善模型的性能。但是,现有的概念瓶颈模型无法在高任务准确性,基于概念的强大解释和对概念的有效干预措施之间找到最佳的妥协,尤其是在稀缺完整和准确的概念主管的现实情况下。为了解决这个问题,我们提出了概念嵌入模型,这是一种新型的概念瓶颈模型,它通过学习可解释的高维概念表示形式而超出了当前的准确性-VS解关性权衡。我们的实验表明,嵌入模型(1)达到更好或竞争性的任务准确性W.R.T. W.R.T.没有概念的标准神经模型,(2)提供概念表示,以捕获有意义的语义,包括其地面真相标签,(3)支持测试时间概念干预措施,其在测试准确性中的影响超过了标准概念瓶颈模型,以及(4)规模对于稀缺的完整概念监督的现实条件。
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非破坏性测试(NDT)被广泛应用于制造和操作过程中涡轮组件的缺陷鉴定。操作效率是燃气轮机OEM(原始设备制造商)的关键。因此,在最小化所涉及的不确定性的同时,尽可能多地自动化检查过程至关重要。我们提出了一个基于视网膜的模型,以识别涡轮叶片X射线图像中的钻孔缺陷。该应用程序是由于大图分辨率而具有挑战性的,在这种分辨率上,缺陷非常小,几乎没有被常用的锚尺寸捕获,并且由于可用数据集的尺寸很小。实际上,所有这些问题在将基于深度学习的对象检测模型应用于工业缺陷数据中非常普遍。我们使用开源模型克服了此类问题,将输入图像分成图块并将其扩展,应用重型数据增强,并使用差分进化器求解器优化锚固尺寸和宽高比。我们用$ 3 $倍的交叉验证验证该模型,显示出非常高的精度,可以识别缺陷的图像。我们还定义了一组最佳实践,可以帮助其他从业者克服类似的挑战。
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在过去的几年中,计算机视觉的显着进步总的来说是归因于深度学习,这是由于大量标记数据的可用性所推动的,并与GPU范式的爆炸性增长配对。在订阅这一观点的同时,本书批评了该领域中所谓的科学进步,并在基于信息的自然法则的框架内提出了对愿景的调查。具体而言,目前的作品提出了有关视觉的基本问题,这些问题尚未被理解,引导读者走上了一个由新颖挑战引起的与机器学习基础共鸣的旅程。中心论点是,要深入了解视觉计算过程,有必要超越通用机器学习算法的应用,而要专注于考虑到视觉信号的时空性质的适当学习理论。
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在各种方法中,旨在使神经网络的学习程序更有效,科学界会根据其估计的复杂性来开发策略,以从较大的网络中蒸发蒸馏知识,或利用对抗机器学习背后的原则。最近提出了一个不同的想法,命名为友好培训,这包括通过增加自动估计的扰动来改变输入数据,其目标是促进神经分类器的学习过程。只要训练收益,转变就会逐渐消失,直到它完全消失。在这项工作中,我们重新审视并扩展了这个想法,引入了通过神经发电机在对抗机器学习的背景下的完全不同和新的方法的启发。我们提出了一种辅助多层网络,该网络负责改变输入数据,使得在训练过程的当前阶段可以更容易地处理分类器。辅助网络与神经分类器共同培训,因此本质上增加了分类器的“深度”,并且预计将在数据改变过程中发现一般规律。辅助网络的效果逐渐减少到训练结束时,当它完全下降时,分类器部署用于应用程序。我们将这种方法称为神经友好培训。涉及多个数据集和不同神经架构的扩展实验程序表明,神经友好培训克服了最初提出的友好培训技术,提高了分类器的泛化,特别是在嘈杂的数据的情况下。
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本文维持了当时征服真正人类的语境中的视觉技能的学习机的位置,其中少数人类对象监督仅由声乐相互作用和指向辅助辅助。这可能需要关于愿景的计算过程的新基础,并通过在简单的人机语言相互作用下在自己的视觉环境中涉及视觉描述的任务中的最终目的。挑战由开发机器组成,该计算机学会在不需要处理视觉数据库的情况下。这可能会向真正正交的竞争轨道打开大门,关于视觉的深度学习技术,不依赖于庞大的视觉数据库的积累。
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对基于机器学习的分类器以及防御机制的对抗攻击已在单一标签分类问题的背景下广泛研究。在本文中,我们将注意力转移到多标签分类,其中关于所考虑的类别中的关系的域知识可以提供自然的方法来发现不连贯的预测,即与培训数据之外的对抗的例子相关的预测分配。我们在框架中探讨这种直觉,其中一阶逻辑知识被转换为约束并注入半监督的学习问题。在此设置中,约束分类器学会满足边际分布的域知识,并且可以自然地拒绝具有不连贯预测的样本。尽管我们的方法在训练期间没有利用任何对攻击的知识,但我们的实验分析令人惊讶地推出了域名知识约束可以有效地帮助检测对抗性示例,特别是如果攻击者未知这样的约束。
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深度学习技术的普及更新了能够处理可以使用图形的复杂结构的神经结构的兴趣,由图形神经网络(GNN)的启发。我们将注意力集中在最初提出的Scarselli等人的GNN模型上。 2009,通过迭代扩散过程编码图表的节点的状态,即在学习阶段,必须在每个时期计算,直到达到学习状态转换功能的固定点,传播信息邻近节点。基于拉格朗日框架的约束优化,我们提出了一种在GNNS中学习的新方法。学习转换功能和节点状态是联合过程的结果,其中通过约束满足机制隐含地表达了状态会聚过程,避免了迭代巨头程序和网络展开。我们的计算结构在由权重组成的伴随空间中搜索拉格朗日的马鞍点,节点状态变量和拉格朗日乘法器。通过加速扩散过程的多个约束层进一步增强了该过程。实验分析表明,该方法在几个基准上的流行模型有利地比较。
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It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.
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Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.
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