The PSL GitHub repository.
guide to learn about using
the PSL software.
Probabilistic soft logic (PSL) is a machine learning framework for developing
probabilistic models. PSL models are easy and fast: you can define them using a
straightforward logical syntax and solve them with fast convex optimization.
PSL has produced state-of-the-art results in many areas spanning natural language
processing, social-network analysis, and computer vision. The PSL framework is
available as an Apache-licensed,
open source project on GitHub with an active
user group for support.
Ready to get started with PSL? Jump to tutorials, key papers, modeling guides, support forum, or source code.
|Overview||Hinge-loss Markov Random Fields and Probabilistic Soft Logic|
|Weight Learning||Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction|
|Online Inference||Budgeted Online Collective Inference|
|Latent Variables||Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs||Topic Modeling||Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models|
See the publications page for more technical information and applications.
We recorded a series of introductory classes on PSL:
The following slides provides more information on PSL: