论文标题
Borch：一种深刻的通用概率编程语言
Borch: A Deep Universal Probabilistic Programming Language
论文作者
论文摘要
自从首次推出多层感知器以来，连接主义者社区一直在不确定性的概念以及如何在这些类型的模型中代表这一概念。在过去的十年中，试图将概率建模的原则性方法与深度神经网络的可扩展性相结合在一起。尽管这种合并的理论益处很明显，但这些努力也有几个重要的实际方面。即，在每个预测中，都会迫使我们创建的模型来表示，学习和报告不确定性。这些努力中的许多是基于扩展了具有其他结构的现有框架。我们提出了Borch，这是一种基于Pytorch之上的可扩展的深层普遍概率编程语言。该代码可在我们的存储库中下载和使用https://gitlab.com/desupervise/borch。
Ever since the Multilayered Perceptron was first introduced the connectionist community has struggled with the concept of uncertainty and how this could be represented in these types of models. This past decade has seen a lot of effort in trying to join the principled approach of probabilistic modeling with the scalable nature of deep neural networks. While the theoretical benefits of this consolidation are clear, there are also several important practical aspects of these endeavors; namely to force the models we create to represent, learn, and report uncertainty in every prediction that is made. Many of these efforts have been based on extending existing frameworks with additional structures. We present Borch, a scalable deep universal probabilistic programming language, built on top of PyTorch. The code is available for download and use in our repository https://gitlab.com/desupervised/borch.
