Probabilistic programming languages (PPLs) have emerged as a transformative tool for expressing complex statistical models and automating inference procedures. By integrating probability theory into ...

Probabilistic programming has emerged as a powerful paradigm for constructing and analysing statistical models by combining the expressiveness of modern programming languages with the rigour of ...

Researchers can demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional ...

Probabilistic programming is an approach to computing based on the idea that probabilistic models can be naturally and efficiently represented as executable code. This idea has enabled researchers to ...

PyAutoFit is a Python based probabilistic programming language for model fitting and Bayesian inference of large datasets. The basic PyAutoFit API allows us a user to quickly compose a probabilistic ...

Abstract: Post-training quantization (PTQ) is an effective solution for deploying deep neural networks on edge devices with limited resources. PTQ is especially attractive because it does not require ...

In this article we give an introduction to the Probabilistic Programming (PP) paradigm for .NET engineers. We start by explaining the differences between PP and traditional approaches and show a ...

Abstract: Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider correlations.