Borch: A Deep Universal Probabilistic Programming Language

The ability to solve a wide variety of challenging real world problems using machine learning has flourished during the course of the past decade. We've seen advancements within diverse application areas, e.g., vision (Bojarski et al. 2016), natural language and physics (Bakarji et al. 2022). We've also seen the emergence of a new paradigm for machine learning where it is possible to teach a computer how to complete mathematical proofs (Davis 2021; Davies et al. 2021) and even compete in a real-world programming competition (Li et al. 2022). Despite the fact that most of these advances were achieved by neural networks, there are still areas where neural networks are far from being superior to more traditional machine learning methods(Shwartz-Ziv and Armon 2021). The strength in many of these methods lies in that they are easier to interpret and reason about.