Coding Theory For Large-scale Machine Learning
Coding theory involves the art and science of how to add redundancy to data to ensure that a desirable output is obtained at despite deviations from ideal behavior from the system components that interact with the data. Through a rich, mathematically elegant set of techniques, coding theory has come to significantly influence the design of modern data communications, compression and storage systems. The last few years have seen a rapidly growing interest in coding theory based approaches for the development of efficient machine learning algorithms towards robust, large-scale, distributed computational pipelines.
The CodML workshop brings together researchers developing coding techniques for machine learning, as well as researchers working on systems implementations for computing, with cutting-edge presentations from both sides. The goal is to learn about non-idealities in system components as well as approaches to obtain reliable and robust learning despite these non-idealities, and identify problems of future interest.
The workshop is co-located with ICML 2019, and will be held in Long Beach, California, USA on June 14th or 15th, 2019.
Please see the website for more details:
Call for Posters
Scope of the Workshop
In this workshop we solicit research papers focused on the …