Recently there has been growing interest in modeling sets with exchangeability such as point clouds. A shortcoming of current approaches is that they restrict the cardinality of the sets considered or can only express limited forms of distribution over unobserved data. To overcome these limitations, we introduce Energy-Based Processes (EBPs), which extend energy based models to exchangeable data while allowing neural network parameterizations of the energy function. A key advantage of these models is the ability to express more flexible distributions over sets without restricting their cardinality. We develop an efficient training procedure for EBPs that demonstrates state-of-the-art performance on a variety of tasks such as point cloud generation, classification, denoising, and image completion.

中文翻译：

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可交换数据的基于能量的过程

最近，人们对具有可交换性的建模集（例如点云）越来越感兴趣。当前方法的一个缺点是它们限制了所考虑集合的基数，或者只能表达对未观察数据的有限分布形式。为了克服这些限制，我们引入了基于能量的过程 (EBP)，它将基于能量的模型扩展到可交换数据，同时允许能量函数的神经网络参数化。这些模型的一个关键优势是能够在不限制其基数的情况下表达更灵活的集合分布。我们为 EBP 开发了一种高效的训练程序，该程序在点云生成、分类、去噪和图像完成等各种任务上展示了最先进的性能。