Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation

Understand

Machine learning algorithms are typically run on large scale, distributed compute infrastructure that routinely face a number of unavailabilities such as failures and temporary slowdowns.

• Adding redundant computations using coding-theoretic tools called "codes" is an emerging technique to alleviate the adverse effects of such unavailabilities.
• A code consists of an encoding function that proactively introduces redundant computation and a decoding function that reconstructs unavailable outputs using the available ones.
• Past work focuses on using codes to provide resilience for linear computations and specific iterative optimization algorithms.