在本文中,我们提出了一种空间图卷积(GC)来对图形上的信号进行分类。现有的GC方法仅限于使用特征空间中的结构信息。另外,GCS的单个步骤仅在目标节点中使用单跳相邻节点上的功能。在本文中,我们提出了两种方法来提高GCS的性能:1)利用特征空间中的结构信息,以及在一个GC步骤中利用多跳信息。在第一种方法中,我们在要素空间中定义三个结构特征:特征角度,特征距离和关系嵌入。第二种方法聚合在GC中的多跳邻居的节点明智特征。两种方法都可以同时使用。我们还提出了图形神经网络(GNNS)集成了所提出的GC来对3D点云和引文网络中的节点进行分类。在实验中,所提出的GNN比现有方法呈现出更高的分类精度。
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图表信号处理是一种普遍存在的任务,如传感器,社会,运输和大脑网络,点云处理和图形神经网络等许多应用程序。通常,图形信号在感测过程中损坏,从而需要恢复。在本文中,我们提出了一种基于深度算法展开(DAU)的图形信号恢复方法。首先,我们通过展开乘法器(ADMM)的交替方向方法的迭代来呈现曲线图信号置位。然后,我们建议通过展开即插即用ADMM(PNP-ADMM)的迭代进行线性劣化的一般恢复方法。在第二种方法中,将展开的基于ADMM的Denoiser纳入子模块,导致嵌套的DAU结构。所提出的去噪/恢复方法中的参数以端到端的方式进行培训。我们的方法是可解释的,并保持参数的数量,因为我们只调谐与图形的正则化参数。我们克服了现有曲线图信号恢复方法中的两个主要挑战:1)由于固定参数,凸优化算法的有限性能由于通常手动确定的固定参数。 2)图形神经网络的大量参数导致训练难度。对曲线信号去噪和插值的几个实验是对合成和真实世界的数据进行的。所提出的方法在两个任务中的根均方误差方面,在几种现有技术上显示了性能改进。
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When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.
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Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own background knowledge about the language and common sense. In this work, we investigate not simply giving common sense, as can be seen in prior research, but also its effective usage. We assume that a part of the environment states different from common sense should constitute one of the grounds for action selection. We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder. DiffG-RL also contains a framework for extracting the appropriate amount and representation of common sense from the source to support the construction of the graph. We validate DiffG-RL in experiments with text-based games that require common sense and show that it outperforms baselines by 17% of scores. The code is available at https://github.com/ibm/diffg-rl
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Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.
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本文档描述了Spotify出于学术研究目的发布的葡萄牙语播客数据集。我们概述了如何采样数据,有关集合的一些基本统计数据,以及有关巴西和葡萄牙方言的分发信息的简要信息。
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深度神经网络(DNN)众所周知,很容易受到对抗例子的影响(AES)。此外,AE具有对抗性可传递性,这意味着为源模型生成的AE可以以非平凡的概率欺骗另一个黑框模型(目标模型)。在本文中,我们首次研究了包括Convmixer在内的模型之间的对抗性转移性的属性。为了客观地验证可转让性的属性,使用称为AutoAttack的基准攻击方法评估模型的鲁棒性。在图像分类实验中,Convmixer被确认对对抗性转移性较弱。
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场景中光的极化信息对于各种图像处理和计算机视觉任务很有价值。平面偏光仪是一种有前途的方法,可以一次性地捕获不同方向的极化图像,而它需要颜色极化的表现。在本文中,我们提出了一个两步的颜色偏振化学网络〜(TCPDNET),该网络由两个颜色的表演和极化演示组成。我们还引入了YCBCR颜色空间中的重建损失,以提高TCPDNET的性能。实验比较表明,TCPDNET在极化图像的图像质量和Stokes参数的准确性方面优于现有方法。
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实现接近真实机器人的高度准确的运动学或模拟器模型可以促进基于模型的控制(例如,模型预测性控制或线性质量调节器),基于模型的轨迹计划(例如,轨迹优化),并减少增强学习方法所需的学习时间。因此,这项工作的目的是学习运动学和/或模拟器模型与真实机器人之间的残余误差。这是使用自动调节和神经网络实现的,其中使用自动调整方法更新神经网络的参数,该方法应用了从无味的Kalman滤波器(UKF)公式进行方程式。使用此方法,我们仅使用少量数据对这些残差错误进行建模 - 当我们直接从硬件操作中学习改善模拟器/运动学模型时,这是必要的。我们演示了关于机器人硬件(例如操纵器组)的方法,并表明,通过学习的残差错误,我们可以进一步缩小运动学模型,模拟和真实机器人之间的现实差距。
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深度神经网络(DNN)众所周知,很容易受到对抗例子的影响(AES)。此外,AE具有对抗性转移性,即为源模型傻瓜(目标)模型生成的AE。在本文中,我们首次研究了为对抗性强大防御的模型的可传递性。为了客观地验证可转让性的属性,使用称为AutoAttack的基准攻击方法评估模型的鲁棒性。在图像分类实验中,使用加密模型的使用不仅是对AE的鲁棒性,而且还可以减少AES在模型的可传递性方面的影响。
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