Gen: a general-purpose probabilistic programming system with programmable inference
By Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka. 2019
213 citations recorded by the source corpus.
Although probabilistic programming is widely used for some restricted classes of statistical models, existing systems lack the flexibility and efficiency needed for practical use with more challenging models arising in fields like computer vision and robotics. This paper introduces Gen, a general-purpose probabilistic programming system that achieves modeling flexibility and inference efficiency via several novel language constructs: (i) the generative function interface for encapsulating probabilistic models; (ii) interoperable modeling languages that strike different flexibility/efficiency trade-offs; (iii) combinators that exploit common patterns of conditional independence; and (iv) an inference library that empowers users to implement efficient inference algorithms at a high level of abstraction. We show that Gen outperforms state-of-the-art probabilistic programming systems, sometimes by multiple orders of magnitude, on diverse problems including object tracking, estimating 3D body pose from a depth image, and inferring the structure of a time series.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Gwonsoo Che
- Martin Hirzel
- Louis Mandel
- Avraham Shinnar
- Javier Burroni
- Guillaume Baudart
- Hongseok Yang
- Matthew J. Parkinson
In Signal Processing
Citations in the corpus
- Foundation Posteriors for Approximate Probabilistic Inference (2022, neutral): “Some require users to assist in the inference process by programmatically specifying conditional independencies [7] or hand-crafting variational distributions [50, 7, 17].”
- Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference (2020, background): “In contrast, other imperative Universal PPLs, such as Church (Goodman et al., 2008), Pyro (Bingham et al., 2019), Turing (Ge et al., 2018), and Gen (Cusumano-Towner et al., 2019), represent the statistical model as a monolithic program that explicitly draws a sample for each random variable that is ”
- Meta-Learning an Inference Algorithm for Probabilistic Programs (2021, background): “…specifying conditional independence in a model (Bingham et al., 2018) or defining proposal or variational distributions (Ritchie et al., 2015; Siddharth et al., 2017; Bingham et al., 2018; Tran et al., 2018; Cusumano-Towner et al., 2019), all enabling users to help inference or learning algorithms.”
- Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming (2021, background): “Generative languages like Church [12], Anglican [32], WebPPL [13], Pyro [3], and Gen [9] describe generative models, i.”