Exploiting redundancy for efficient processing of DNNs and beyond

Jun 15, 2019

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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.

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About ICML 2019

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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