In a sense, it sounds like that’s another facet of computational thinking that’s more relevant in the age of AI—the abstractions of statistics and probability in addition to algorithms and data ...

We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our aims are to develop ...

Abstract: Probabilistic programming systems allow developers to model random phenomena and perform reasoning about the model efficiently. As the number of probabilistic programming systems is growing ...

Ising machines demonstrate significant potential to tackle computationally complex challenges, including combinatorial optimization problems related to logistics, manufacturing, finance, and AI. The ...

This is the community edition of GenJAX, a probabilistic programming language in development at MIT's Probabilistic Computing Project. We recommend this version for stability, community contributions, ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Satellite data provides essential insights into the spatiotemporal distribution of CO ...

Imagine a world where your computer doesn’t just work harder but smarter, tapping into the very chaos that surrounds us. It’s not science fiction—it’s the dawn of probabilistic and thermodynamic ...

Generative models of tabular data are key in Bayesian analysis, probabilistic machine learning, and fields like econometrics, healthcare, and systems biology. Researchers have developed methods to ...

This repository contains the JAX implementation that accompanies the paper Probabilistic programming with programmable variational inference, as well as the experiments used to generate figures and ...

Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: ...