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Borch: A Deep Universal Probabilistic Programming Language

arXiv 2209.06168 ↗First posted 13 Sep 2022cs.AI cs.LG cs.PLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 13 Sep 2022.

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19 of 34 references.

References · 19

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7 more
  1. 2022Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoderscited by 58
  2. 2016Edward: A library for probabilistic modeling, inference, and criticismcited by 28
  3. 2016Design and Implementation of Probabilistic Programming Language Anglicancited by 15
  4. 2021A Concise Review of Transfer Learningcited by 13
  5. 2016Maximum a Posteriori Estimators as a Limit of Bayes Estimatorscited by 12
  6. 2022Performance Analysis of Out-of-Distribution Detection on Trained Neural Networkscited by 7
  7. 2021Deep Learning and Mathematical Intuition: A Review of (Davies et al. 2021)cited by 1

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