过去,现实世界中社交网络的图表错过了两个重要元素:连接的多重性和表示时间。为此,在本文中,我们为社交网络提供了一个新的动态异质图表示,其中包括图形的每个组件中的时间,即节点和边缘,每种捕获异质性的不同类型。我们通过提出四个与时间有关的查询和深度学习问题来说明这种表示的力量,这些查询和深度学习问题无法轻易在常规的均匀图表中处理。作为概念的证明,我们介绍了新的社交媒体平台(Steemit)的详细表示,我们用它来说明动态查询功能以及使用图形神经网络(GNNS)的预测任务。结果说明了动态异质图表示对社交网络的模型的力量。鉴于这是一个相对研究的领域,我们还说明了在查询优化方面的未来工作以及异质图结构的新动态预测任务的机会。
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视网膜眼底图像的自动评估是涌现为最重要的早期检测和治疗渐进眼疾病的工具之一。青光眼导致视力的进步退化,其特征在于光学杯形状的变形和血管的变性导致沿神经垂体边缘形成凹口的形成。在本文中,我们提出了一种基于深度学习的管道,用于从数字眼底图像(DFIS)的光盘(OD)和光学杯(OC)区域的自动分割,从而提取预测青光眼所需的不同特征。该方法利用了神经古代轮辋的局灶性凹口分析以及杯盘比值值作为分类参数,以提高计算机辅助设计(CAD)系统的准确性分析青光眼。支持基于向量的机器学习算法用于分类,基于提取的功能将DFIS分类为青光眼或正常。在自由可用的DRISHTI-GS数据集上评估了所提出的管道,得到了从DFIS检测青光眼的93.33%的精度。
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Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are in turn used for designing and verifying autonomous systems. This is particularly useful in modeling medical systems where data can be leveraged to individualize treatment. In safety-critical applications, it is important that the data-driven model is conformant to established knowledge from the natural sciences. Such knowledge is often available or can often be distilled into a (possibly black-box) model $M$. For instance, the unicycle model for an F1 racing car. In this light, we consider the following problem - given a model $M$ and state transition dataset, we wish to best approximate the system model while being bounded distance away from $M$. We propose a method to guarantee this conformance. Our first step is to distill the dataset into few representative samples called memories, using the idea of a growing neural gas. Next, using these memories we partition the state space into disjoint subsets and compute bounds that should be respected by the neural network, when the input is drawn from a particular subset. This serves as a symbolic wrapper for guaranteed conformance. We argue theoretically that this only leads to bounded increase in approximation error; which can be controlled by increasing the number of memories. We experimentally show that on three case studies (Car Model, Drones, and Artificial Pancreas), our constrained neurosymbolic models conform to specified $M$ models (each encoding various constraints) with order-of-magnitude improvements compared to the augmented Lagrangian and vanilla training methods.
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We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of solution functions of linear programming (LP) and quadratic programming (QP) is a universal approximant for the $C^k$ smooth model class or some restricted Sobolev space, and we characterize the rate-distortion, \emph{(2)} the approximation power is investigated through a viewpoint of regression error, where information about the target function is provided in terms of data observations, \emph{(3)} compositionality in the form of a deep architecture with optimization as a layer is shown to reconstruct some basic functions used in numerical analysis without error, which implies that \emph{(4)} a substantial reduction in rate-distortion can be achieved with a universal network architecture, and \emph{(5)} we discuss the statistical bounds of empirical covering numbers for LP/QP, as well as a generic optimization problem (possibly nonconvex) by exploiting tame geometry. Our results provide the \emph{first rigorous analysis of the approximation and learning-theoretic properties of solution functions} with implications for algorithmic design and performance guarantees.
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Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.
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The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.
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Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory Computing (IMC) platforms can serve as energy-efficient inference engines, they are accursed by the immense energy, latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing the benefits of in-memory computations. We propose a hardware/software co-design methodology to deploy SNNs into an ADC-Less IMC architecture using sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating the above issues. Our proposed framework incurs minimal accuracy degradation by performing hardware-aware training and is able to scale beyond simple image classification tasks to more complex sequential regression tasks. Experiments on complex tasks of optical flow estimation and gesture recognition show that progressively increasing the hardware awareness during SNN training allows the model to adapt and learn the errors due to the non-idealities associated with ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and latency improvements, $2-7\times$ and $8.9-24.6\times$, respectively, depending on the SNN model and the workload, compared to HP-ADC IMC.
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Existing regulations prohibit model developers from accessing protected attributes (gender, race, etc.), often resulting in fairness assessments on populations without knowing their protected groups. In such scenarios, institutions often adopt a separation between the model developers (who train models with no access to the protected attributes) and a compliance team (who may have access to the entire dataset for auditing purpose). However, the model developers might be allowed to test their models for bias by querying the compliance team for group fairness metrics. In this paper, we first demonstrate that simply querying for fairness metrics, such as statistical parity and equalized odds can leak the protected attributes of individuals to the model developers. We demonstrate that there always exist strategies by which the model developers can identify the protected attribute of a targeted individual in the test dataset from just a single query. In particular, we show that one can reconstruct the protected attributes of all the individuals from O(Nk log n/Nk) queries when Nk<<n using techniques from compressed sensing (n: size of the test dataset, Nk: size of smallest group). Our results pose an interesting debate in algorithmic fairness: should querying for fairness metrics be viewed as a neutral-valued solution to ensure compliance with regulations? Or, does it constitute a violation of regulations and privacy if the number of queries answered is enough for the model developers to identify the protected attributes of specific individuals? To address this supposed violation, we also propose Attribute-Conceal, a novel technique that achieves differential privacy by calibrating noise to the smooth sensitivity of our bias query, outperforming naive techniques such as Laplace mechanism. We also include experimental results on the Adult dataset and synthetic data (broad range of parameters).
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已经证明,深层合奏将典型的集体学习中看到的积极效果扩展到神经网络和增强学习(RL)。但是,要提高此类整体模型的效率仍然有很多事情要做。在这项工作中,我们介绍了在RL(feft)中快速传输的各种合奏,这是一种基于合奏的新方法,用于在高度多模式环境中进行增强学习,并改善了转移到看不见的环境。该算法分为两个主要阶段:合奏成员的培训,以及合成成员的合成(或微调)成员,以在新环境中起作用。该算法的第一阶段涉及并行培训常规的政策梯度或参与者 - 批评者,但增加了鼓励这些政策彼此不同的损失。这会导致单个单峰剂探索最佳策略的空间,并捕获与单个参与者相比,捕获环境的多模式的更多。 DEFT的第二阶段涉及将组件策略综合为新的策略,该策略以两种方式之一在修改的环境中效果很好。为了评估DEFT的性能,我们从近端策略优化(PPO)算法的基本版本开始,并通过faft的修改将其扩展。我们的结果表明,预处理阶段可有效地在多模式环境中产生各种策略。除了替代方案,faft通常会收敛到高奖励的速度要快得多,例如随机初始化而无需faft和合奏成员的微调。虽然当然还有更多的工作来分析理论上的熟练并将其扩展为更强大,但我们认为,它为在环境中捕获多模式的框架提供了一个强大的框架,同时仍将使用简单策略表示的RL方法。
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我们提出了一个新型混合动力系统(硬件和软件),该系统载有微型无人接地车辆(MiniUGV),以执行复杂的搜索和操纵任务。该系统利用异质机器人来完成使用单个机器人系统无法完成的任务。它使无人机能够探索一个隐藏的空间,并具有狭窄的开口,Miniugv可以轻松进入并逃脱。假定隐藏的空间可用于MiniUGV。 MiniUGV使用红外(IR)传感器和单眼相机在隐藏空间中搜索对象。所提出的系统利用摄像机的更广阔的视野(FOV)以及对象检测算法的随机性引导隐藏空间中的MiniUGV以找到对象。找到对象后,MiniUGV使用视觉伺服抓住它,然后返回其起点,从无人机将其缩回并将物体运送到安全的地方。如果在隐藏空间中没有发现对象,则无人机继续进行空中搜索。束缚的MiniUGV使无人机具有超出其影响力并执行搜索和操纵任务的能力,而该任务对于任何机器人都无法单独进行。该系统具有广泛的应用,我们通过重复实验证明了其可行性。
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