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The Role of Stochastic Rounding in Robust Machine Learning
Stochastic rounding is a rounding technique that has gained increasing attention in the machine learning community due to its potential for achieving better accuracy and efficiency than traditional rounding methods. In this survey, we provide an overview of stochastic rounding techniques in machine learning. We will discuss the practical considerations of using stochastic rounding in machine learning, including the implementation details, the impact on computational efficiency, and the potential trade-offs with accuracy. This highlights the benefits of using stochastic rounding in machine learning, such as improved accuracy, faster convergence, and reduced memory usage. Overall, this survey aims to provide a comprehensive and up-to-date overview of stochastic rounding in machine learning and to stimulate further research in this promising area.