本文证明了基于网络的多智能体设置中一类非凸优化的分布式算法的全局最优收敛性。允许使者通过时变无向图进行通信。假设Eachagent具有局部目标函数(假设是平滑的,但可能是非凸的)。本文考虑了优化求和函数的算法。提出了一种共识+创新类型的分布式算法,它依赖于代理级别的一阶信息。在网络连通性和成本目标的适当条件下,通过退火型方法实现对全局最优的收敛,并且在每个代理的更新步骤中独立地添加衰减高斯噪声。表明所提出的算法在概率上收敛于和函数的全局最小值集合。
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In social settings, individuals interact through webs of relationships. Eachindividual is a node in a complex network (or graph) of interdependencies andgenerates data, lots of data. We label the data by its source, or formallystated, we index the data by the nodes of the graph. The resulting signals(data indexed by the nodes) are far removed from time or image signals indexedby well ordered time samples or pixels. DSP, discrete signal processing,provides a comprehensive, elegant, and efficient methodology to describe,represent, transform, analyze, process, or synthesize these well ordered timeor image signals. This paper extends to signals on graphs DSP and its basictenets, including filters, convolution, z-transform, impulse response, spectralrepresentation, Fourier transform, frequency response, and illustrates DSP ongraphs by classifying blogs, linear predicting and compressing data fromirregularly located weather stations, or predicting behavior of customers of amobile service provider.
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Gossip algorithms are attractive for in-network processing in sensor networksbecause they do not require any specialized routing, there is no bottleneck orsingle point of failure, and they are robust to unreliable wireless networkconditions. Recently, there has been a surge of activity in the computerscience, control, signal processing, and information theory communities,developing faster and more robust gossip algorithms and deriving theoreticalperformance guarantees. This article presents an overview of recent work in thearea. We describe convergence rate results, which are related to the number oftransmitted messages and thus the amount of energy consumed in the network forgossiping. We discuss issues related to gossiping over wireless links,including the effects of quantization and noise, and we illustrate the use ofgossip algorithms for canonical signal processing tasks including distributedestimation, source localization, and compression.
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视觉对话需要使用对话历史作为上下文来回答基于图像的一系列问题。除了在视觉问答(VQA)中发现的挑战(可以看作是一轮对话),视觉表盘还可以包含更多。我们关注一个称为视觉干扰分辨率的问题,该问题涉及确定哪些词,通常是名词短语和代词,共同引用图像中的同一实体/对象实例。这是至关重要的,特别是对于代词(例如,“它”),如对话代理必须首先将它链接到先前的共同参考(例如,“船”),然后才能依靠共同参与“船”的视觉基础来推断代词`的'。先前的工作(在视觉对话中)模拟视觉共参考解决方案(a)通过历史记录的内存网络隐含地,或(b)整个问题的粗略级别;而不是明确地在词组级别的粒度。在这项工作中,我们提出了一种用于视觉对话的神经模块网络架构,引入了两个新颖的模块 - 参考和排除 - 在更精细的单词级别执行显式的,基础的共参考分辨率。我们通过实现近乎完美的精确度来展示我们的模型在MNIST Dialog上的有效性,这是一个视觉上简单但有思想的复杂数据集,以及onVisDial,一个在真实图像上的大型且具有挑战性的视觉对话数据集,其中我们的模型优于其他方法,并且更易于解释,定性的,坚定的,一致的。
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存在潜变量的半参数非线性回归模型应用于学习网络图结构。这些潜变量可以对应于相互作用实体的复杂系统中的未建模现象或未测量的现象。该公式联合估计基础数据生成中的非线性,测量实体之间的直接相互作用,以及未测量过程对观察数据的间接影响。学习被认为是规范化的经验风险最小化。概述了用于学习模型的算法的细节。实验证明了学习模型对实际数据的性能。
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We introduce the first goal-driven training for visual question answering anddialog agents. Specifically, we pose a cooperative 'image guessing' gamebetween two agents -- Qbot and Abot -- who communicate in natural languagedialog so that Qbot can select an unseen image from a lineup of images. We usedeep reinforcement learning (RL) to learn the policies of these agentsend-to-end -- from pixels to multi-agent multi-round dialog to game reward. We demonstrate two experimental results. First, as a 'sanity check' demonstration of pure RL (from scratch), we showresults on a synthetic world, where the agents communicate in ungroundedvocabulary, i.e., symbols with no pre-specified meanings (X, Y, Z). We findthat two bots invent their own communication protocol and start using certainsymbols to ask/answer about certain visual attributes (shape/color/style).Thus, we demonstrate the emergence of grounded language and communication among'visual' dialog agents with no human supervision. Second, we conduct large-scale real-image experiments on the VisDial dataset,where we pretrain with supervised dialog data and show that the RL 'fine-tuned'agents significantly outperform SL agents. Interestingly, the RL Qbot learns toask questions that Abot is good at, ultimately resulting in more informativedialog and a better team.
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We introduce the task of Visual Dialog, which requires an AI agent to hold ameaningful dialog with humans in natural, conversational language about visualcontent. Specifically, given an image, a dialog history, and a question aboutthe image, the agent has to ground the question in image, infer context fromhistory, and answer the question accurately. Visual Dialog is disentangledenough from a specific downstream task so as to serve as a general test ofmachine intelligence, while being grounded in vision enough to allow objectiveevaluation of individual responses and benchmark progress. We develop a noveltwo-person chat data-collection protocol to curate a large-scale Visual Dialogdataset (VisDial). VisDial v0.9 has been released and contains 1 dialog with 10question-answer pairs on ~120k images from COCO, with a total of ~1.2M dialogquestion-answer pairs. We introduce a family of neural encoder-decoder models for Visual Dialog with3 encoders -- Late Fusion, Hierarchical Recurrent Encoder and Memory Network --and 2 decoders (generative and discriminative), which outperform a number ofsophisticated baselines. We propose a retrieval-based evaluation protocol forVisual Dialog where the AI agent is asked to sort a set of candidate answersand evaluated on metrics such as mean-reciprocal-rank of human response. Wequantify gap between machine and human performance on the Visual Dialog taskvia human studies. Putting it all together, we demonstrate the first 'visualchatbot'! Our dataset, code, trained models and visual chatbot are available onhttps://visualdialog.org
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虽然目前的通用游戏(GGP)系统促进了用于游戏的人工智能(AI)的有用研究,但它们通常是特定的,并且计算效率低。在本文中,我们描述了一个名为Ludii的“ludemic”通用游戏系统的初始版本,该系统具有为AI研究人员以及相关领域的游戏设计师,历史学家,教育工作者和从业者提供有效工具的潜力。 Ludiidefines游戏作为ludemes的结构,即高级,易于理解的游戏概念。我们通过概述其主要优点来建立Ludii的基础:通用性,可扩展性,可理解性和效率。实验上,Ludii优于Tiltyard GGP存储库中所有可用游戏的基于命题网络的最有效的Game DescriptionLanguage(GDL)reasoners之一。
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带有冲突的装箱(BPC)是这样的问题,其中具有兼容性约束的物品必须包装在最少数量的箱中,注意箱的容量并确保在每个箱中包装非冲突物品。在这项工作中,我们介绍了Bin Packing Problem withCompatible Categories(BPCC),这是BPC的一个变体,其中的项目属于toconflicting或兼容的类别,与之前文献中的逐项兼容性相反。在位于人口密集区域的纳米存储体的最后一英里分布的背景中,这是一个常见问题。为了有效地解决实际大小的问题实例,我们提出了一种变邻域搜索(VNS)元启发式算法。计算实验表明,与在高性能计算环境中运行的线性整数规划相比,该算法在很短的时间内产生了良好的解决方案。
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本文描述了一个原型系统,它将社交媒体分析集成到欧洲洪水意识系统(EFAS)中。这种集成允许社交媒体数据的收集由水文气象模型确定的洪水风险警告自动触发。然后,我们采用多语言方法,通过采用两种最先进的方法来找到与洪水相关的信息:语言不可知的词嵌入和语言对齐的词嵌入。这两种方法都可以用于引导社交媒体消息的分类器,用于具有很少或没有标记数据的新语言。最后,我们描述了一种选择相关和代表性消息并在EFAS界面中显示它们的方法。
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