Augur: Data-Parallel Probabilistic Modeling

By Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Craig Pocock, Stephen Joseph Green, Guy L. Steele Jr.. 2014

29 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, ML Systems & Hardware

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. David M. Blei
2. Dustin Tran
3. Matthew D. Hoffman
4. Rajesh Ranganath
5. Kevin P. Murphy
6. Rif A. Saurous
7. Michalis K. Titsias
8. Alexey Radul

In ML Systems & Hardware

1. Martin C. Rinard
2. Vikash K. Mansinghka
3. Alexey Radul
4. David M. Blei
5. Ulrich Schaechtle
6. Yutian Chen
7. Matthew D. Hoffman
8. Shivam Handa

Citations in the corpus

• Deep Probabilistic Programming (2017, methodology): “This prevents reifying the full model, which can lead to unreasonable memory consumption (Tristan et al., 2014).”
• Gen: a general-purpose probabilistic programming system with programmable inference (2019, background): “Languages that support a limited model class and only a few inference algorithms can perform some of this tuning automatically to improve efficiency [8, 62].”
• Probabilistic programming with programmable inference (2018, methodology): “…better support this increasingly prominent class of computations, researchers have designed and implemented probabilistic programming languages, which provide direct support for defining probabilistic models and performing probabilistic inference within the language [3, 16– 19, 23, 28, 31, 47, 50].”