与人类类似,动物的面部表情与情绪状态紧密相关。但是,与人类领域相反,动物面部表情对情绪状态的自动识别是没有充满反应的,这主要是由于数据收集和建立地面真相的困难,涉及非语言用户的情绪状态。我们将最近的深度学习技术应用于在受控的实验环境中收集的数据集上对狗的挫败进行分类和(负面)的挫败感。我们探索在此任务的不同监督下不同骨干(例如,重新连接,VIT)的适用性,并发现自我监督的预定的VIT(DINO-VIT)的特征优于其他替代方案。据我们所知,这项工作是第一个解决对受控实验中获得的数据自动分类的任务。
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Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems including group DRO, subpopulation fairness, and empirical conditional value at risk (CVaR) optimization. Our new algorithms achieve faster convergence rates than existing algorithms for multiple DRO settings. We also provide a new information-theoretic lower bound that implies our bounds are tight for group DRO. Empirically, too, our algorithms outperform known methods
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Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to attain the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one. To avoid these deviations, the shape of the die can be computationally optimized, which has already been investigated in the literature using classical optimization approaches. A new approach in the field of shape optimization is the utilization of Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment. While not necessarily superior to classical, e.g., gradient-based or evolutionary, optimization algorithms for one single problem, RL techniques are expected to perform especially well when similar optimization tasks are repeated since the agent learns a more general strategy for generating optimal shapes instead of concentrating on just one single problem. In this work, we investigate this approach by applying it to two 2D test cases. The flow-channel geometry can be modified by the RL agent using so-called Free-Form Deformation, a method where the computational mesh is embedded into a transformation spline, which is then manipulated based on the control-point positions. In particular, we investigate the impact of utilizing different agents on the training progress and the potential of wall time saving by utilizing multiple environments during training.
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Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.
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在本文中,我们解决了多视图3D形状重建的问题。尽管最近与隐式形状表示相关的最新可区分渲染方法提供了突破性的表现,但它们仍然在计算上很重,并且在估计的几何形状上通常缺乏精确性。为了克服这些局限性,我们研究了一种基于体积的新型表示形式建立的新计算方法,就像在最近的可区分渲染方法中一样,但是用深度图进行了参数化,以更好地实现形状表面。与此表示相关的形状能量可以评估给定颜色图像的3D几何形状,并且不需要外观预测,但在优化时仍然受益于体积整合。在实践中,我们提出了一个隐式形状表示,SRDF基于签名距离,我们通过沿摄像头射线进行参数化。相关的形状能量考虑了深度预测一致性和光度一致性之间的一致性,这是在体积表示内的3D位置。可以考虑各种照片一致先验的基础基线,或者像学习功能一样详细的标准。该方法保留具有深度图的像素准确性,并且可行。我们对标准数据集进行的实验表明,它提供了有关具有隐式形状表示的最新方法以及传统的多视角立体方法的最新结果。
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深度学习的概括分析通常假定训练会收敛到固定点。但是,最近的结果表明,实际上,用随机梯度下降优化的深神经网络的权重通常无限期振荡。为了减少理论和实践之间的这种差异,本文着重于神经网络的概括,其训练动力不一定会融合到固定点。我们的主要贡献是提出一个统计算法稳定性(SAS)的概念,该算法将经典算法稳定性扩展到非convergergent算法并研究其与泛化的联系。与传统的优化和学习理论观点相比,这种崇高的理论方法可导致新的见解。我们证明,学习算法的时间复杂行为的稳定性与其泛化有关,并在经验上证明了损失动力学如何为概括性能提供线索。我们的发现提供了证据表明,即使训练无限期继续并且权重也不会融合,即使训练持续进行训练,训练更好地概括”的网络也是如此。
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将有用的背景知识传达给加强学习(RL)代理是加速学习的重要方法。我们介绍了Rlang,这是一种特定领域的语言(DSL),用于将域知识传达给RL代理。与RL社区提出的其他现有DSL不同,该基础是决策形式主义的单个要素(例如,奖励功能或政策功能),RLANG可以指定有关马尔可夫决策过程中每个元素的信息。我们为rlang定义了精确的语法和基础语义,并提供了解析器实施,将rlang程序基于算法 - 敏捷的部分世界模型和政策,可以由RL代理利用。我们提供一系列示例RLANG程序,并演示不同的RL方法如何利用所得的知识,包括无模型和基于模型的表格算法,分层方法和深度RL算法(包括策略梯度和基于价值的方法)。
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将离散域上的功能集成到神经网络中是开发其推理离散对象的能力的关键。但是,离散域是(1)自然不适合基于梯度的优化,并且(2)与依赖于高维矢量空间中表示形式的深度学习体系结构不相容。在这项工作中,我们解决了设置功能的两个困难,这些功能捕获了许多重要的离散问题。首先,我们开发了将设置功能扩展到低维连续域的框架,在该域中,许多扩展是自然定义的。我们的框架包含许多众所周知的扩展,作为特殊情况。其次,为避免不良的低维神经网络瓶颈,我们将低维扩展转换为高维空间中的表示形式,从半际计划进行组合优化的成功中获得了灵感。从经验上讲,我们观察到扩展对无监督的神经组合优化的好处,特别是具有高维其表示。
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本文介绍了一种生成高度选择性编码的方法,这些编码可以在物理模块上磁性地“编程”,以使其能够以所选的配置自组装。我们基于Hadamard矩阵生成这些编码,并展示如何设计模块的面孔,以对其预期的伴侣具有最大吸引力,同时对其他面孔保持最大不可知论。我们得出这些界限的保证,并通过实验验证它们的吸引力和不可知论。使用其面部已被软磁性材料覆盖的立方模块,我们显示了如何使用带有平面面的廉价的被动模块来选择性地自组装成目标形状,而无需几何指南。我们表明,这些模块可以使用基于CNC的磁性绘图仪轻松地重新编程,以用于新的目标形状,并证明水箱中8个立方体的自组装。
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本文介绍了基于立方体的可重构机器人,该机器人利用基于电磁体的驱动框架通过枢轴在三个维度重新配置。尽管已经探索了各种可自我配置机器人的驱动机制,但它们通常会遭受成本,复杂性,组装和尺寸要求,以阻止这种机器人的生产规模。为了应对这一挑战,我们使用基于嵌入每个立方体边缘的电磁体的驱动机制来互换创建相同或相反极化的电磁体对,分别产生排斥或吸引力。通过利用铰链形成的吸引力,并以驱动旋转操作的排斥力,我们可以通过电磁磁性磁极磁极的旋转旋转来重新配置机器人,并使其构成模块(称为电动员)。为了证明这一点,我们开发了完全不受限制的三维自我恢复的机器人,并在抛物线能飞行中使用枢轴和横向动作展示了2D和3D自我调查的方法。本文介绍了我们机器人的硬件设计,其旋转框架,我们的重新配置计划软件以及对系统的动态和电气特性的评估,以告知可扩展的自我可控机器人的设计。
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