Probabilistic Programming for Advancing Machine Learning
Machine Learning is at the heart of modern approaches to artificial intelligence. The field posits that teaching computers how to learn can be significantly more effective than programming them explicitly. Unfortunately, building effective machine learning applications currently still requires Herculean efforts on the part of highly trained experts in machine learning.
Probabilistic Programming is a new programming paradigm for managing uncertain information. The goal of the Probabilistic Programming for Advancing Machine Learning (PPAML) program is to facilitate the construction of machine learning applications by using probabilistic programming to:
The PPAML program started in November 2013 and is scheduled to run 46 months, with three phases of activity through 2017.
|About PPAML||Challenge Problem Descriptions|
|Program Overview||#1. Quad-Rotor Sensor Fusion|
|Announcements||#2. Continent-Scale Bird Migration Modeling|
|Summer Schools||#3. Wide Area Motion Imagery Track Linking|
|Milestones||#4. Small Problems Collection|
|Presentations||#5. Natural Language Processing|
|Resources||#6. Image Labeling|
|Press||#7. Flu Spread|
|#8. Desktop Activity Recognition|
|Problem #9 2017 January|
|Problem #10 2017 July|
Modified Tue Jul 12 12:37:59 2016 by Eric.Woldridge.