Privacy-preserving machine learning in data-sharing processes is an ever-critical task that enables collaborative training of Machine Learning (ML) models without the need to share the original data sources. It is especially relevant when an organization must assure that sensitive data remains private throughout the whole ML pipeline, i.e., training and inference phases. This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacy-preserving embedded data. Thus, organizations can share the data representation to increase machine learning models' performance in scenarios with more than one data source for a shared predictive downstream task.
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Numerical simulations are ubiquitous in science and engineering. Machine learning for science investigates how artificial neural architectures can learn from these simulations to speed up scientific discovery and engineering processes. Most of these architectures are trained in a supervised manner. They require tremendous amounts of data from simulations that are slow to generate and memory greedy. In this article, we present our ongoing work to design a training framework that alleviates those bottlenecks. It generates data in parallel with the training process. Such simultaneity induces a bias in the data available during the training. We present a strategy to mitigate this bias with a memory buffer. We test our framework on the multi-parametric Lorenz's attractor. We show the benefit of our framework compared to offline training and the success of our data bias mitigation strategy to capture the complex chaotic dynamics of the system.
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In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users. For our case study, we analyzed a total number of 16.4k users and 48.7k customer support conversations within the financial vertical of a large e-commerce company in Latin America. Consequently, our main contributions and objectives are to use Natural Language Processing (NLP) to assess and predict the recommendation behavior where, in addition to using static sentiment analysis, we exploit the predictive power of each user's sentiment dynamics. Our results show that, with respective feature interpretability, it is possible to predict the likelihood of a user to recommend a product or service, based solely on the message-wise sentiment evolution of their CS conversations in a fully automated way.
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We consider a radio resource management (RRM) problem in a multi-user wireless network, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints. We provide theoretical justification for the feasibility and near-optimality of the RRM decisions generated by the proposed state-augmented algorithm. Focusing on the power allocation problem with RRM policies parameterized by a graph neural network (GNN) and dual variables sampled from the dual descent dynamics, we numerically demonstrate that the proposed approach achieves a superior trade-off between mean, minimum, and 5th percentile rates than baseline methods.
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Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 um2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.
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图卷积学习导致了各个领域的许多令人兴奋的发现。但是,在某些应用中,传统图不足以捕获数据的结构和复杂性。在这种情况下,多编码自然出现是可以嵌入复杂动力学的离散结构。在本文中,我们开发了有关多编码的卷积信息处理,并引入了卷积多编码神经网络(MGNN)。为了捕获每个多数边缘内外的信息传播的复杂动力学,我们正式化了一个卷积信号处理模型,从而定义了多格画上信号,过滤和频率表示的概念。利用该模型,我们开发了多个学习架构,包括采样程序以降低计算复杂性。引入的体系结构用于最佳无线资源分配和仇恨言语本地化任务,从而比传统的图形神经网络的性能提高了。
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在智能的建筑管理中,了解房间的人数及其位置对于更好地控制其照明,通风和供暖,并以降低的成本和改善的舒适度很重要。这通常是通过使用安装在房间天花板上的紧凑型嵌入式设备并集成低分辨率红外摄像机的人员来实现的,从而掩盖了每个人的身份。但是,为了准确检测,最新的深度学习模型仍然需要使用大量注释的图像数据集进行监督培训。在本文中,我们研究了适用于基于低分辨率红外图像的人检测的具有成本效益的方法。结果表明,对于此类图像,我们可以减少监督和计算的量,同时仍然达到高水平的检测准确性。从需要图像中每个人的边界框注释的单杆探测器,到仅依靠不包含人的未标记图像的自动编码器,可以在注释成本方面节省大量,以及计算较低的模型费用。我们在具有低分辨率红外图像的两个具有挑战性的顶级数据集上验证了这些实验发现。
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序数模式的统计分析的最终目的是表征它们诱导的特征的分布。特别是,了解大类时间序列模型的对熵统计复杂性的联合分布将允许迄今无法获得的统计测试。在这个方向上工作,我们表征了Shannon经验的渐进分布,用于任何模型,在此模型中,真正的归一化熵既不为零也不为零。我们从中心极限定理(假设大时间序列),多元增量方法和其平均值的三阶校正获得了渐近分布。我们讨论了其他结果(精确,一阶和二阶校正)有关其准确性和数值稳定性的适用性。在建立有关香农熵的测试统计数据的一般框架内,我们提出了双边测试,该测试验证是否有足够的证据拒绝以下假设,即两个信号产生了具有相同Shannon熵的顺序模式。我们将此双边测试应用于来自三个城市(都柏林,爱丁堡和迈阿密)的每日最高温度时间序列,并获得了明智的结果。
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模型校准衡量预测的概率估计与真实性可能性之间的一致性。正确的模型校准对于高风险应用至关重要。不幸的是,现代深层神经网络的校准不佳,损害了可信度和可靠性。由于组织边界的自然不确定性,医疗图像分割尤其遭受了这种情况。这对他们的损失功能感到愤怒,这有利于多数级别的过度自信。我们用Domino(一种域感知的模型校准方法)解决了这些挑战,该方法利用了类标签之间的语义混淆性和分层相似性。我们的实验表明,在头部图像分割中,我们受多米诺骨牌校准的深神经网络优于非校准模型和最先进的形态学方法。我们的结果表明,与这些方法相比,我们的方法可以始终如一地实现更好的校准,更高的准确性和更快的推理时间,尤其是在稀有类别上。该性能归因于我们的域知觉正规化,以告知语义模型校准。这些发现表明,班级标签之间语义联系在建立深度学习模型的信心中的重要性。该框架有可能提高通用医学图像分割模型的可信度和可靠性。本文的代码可在以下网址获得:https://github.com/lab-smile/domino。
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我们研究了数据驱动的深度学习方法的潜力,即从观察它们的混合物中分离两个通信信号。特别是,我们假设一个信号之一的生成过程(称为感兴趣的信号(SOI)),并且对第二个信号的生成过程不了解,称为干扰。单通道源分离问题的这种形式也称为干扰拒绝。我们表明,捕获高分辨率的时间结构(非平稳性),可以准确地同步与SOI和干扰,从而带来了可观的性能增长。有了这个关键的见解,我们提出了一种域信息神经网络(NN)设计,该设计能够改善“现成” NNS和经典检测和干扰拒绝方法,如我们的模拟中所示。我们的发现突出了特定于交流领域知识的关键作用在开发数据驱动的方法方面发挥了作用,这些方法具有前所未有的收益的希望。
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