论文标题
查找印emo：使用能量移动距离的神经估计的几何拟合
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover's Distance
论文作者
论文摘要
最近开发了一种新型的神经结构，该神经结构通过以最小的方式限制其权重的标准，从而在模型的Lipschitz常数上实施了精确的上限，与其他技术相比，表达式更高。我们为这种架构提供了一个新的有趣的方向：通过采用Kantorovich-Rubinstein二重性来实现其在几何拟合应用中的使用，对最佳运输的Wasserstein Metric（Earth Mover's距离）进行了最佳运输。具体而言，我们专注于高能粒子物理学的领域，在该领域中，可以根据瓦斯斯坦公制（Wasserstein Metric）定义粒子卖方事件空间的度量，该指标被称为能量移动器的距离（EMD）。这个仪表有潜力彻底改变数据驱动的对撞机现象学。这里介绍的工作是通过提供直接计算EMD的可区分方法来实现这一目标的重要一步。我们展示了如何使用我们的方法来开发新型聚类算法的灵活性。
A novel neural architecture was recently developed that enforces an exact upper bound on the Lipschitz constant of the model by constraining the norm of its weights in a minimal way, resulting in higher expressiveness compared to other techniques. We present a new and interesting direction for this architecture: estimation of the Wasserstein metric (Earth Mover's Distance) in optimal transport by employing the Kantorovich-Rubinstein duality to enable its use in geometric fitting applications. Specifically, we focus on the field of high-energy particle physics, where it has been shown that a metric for the space of particle-collider events can be defined based on the Wasserstein metric, referred to as the Energy Mover's Distance (EMD). This metrization has the potential to revolutionize data-driven collider phenomenology. The work presented here represents a major step towards realizing this goal by providing a differentiable way of directly calculating the EMD. We show how the flexibility that our approach enables can be used to develop novel clustering algorithms.
