What is it about?
This paper evaluates the performance of AdaGrad, AMSGrad, RMSProp, and AdaDelta algorithms on five distinct data distributions (three Gaussian, two exponential). The study demonstrates that incorporating cosine annealing significantly enhances the performance of most algorithms, providing valuable insights for optimizing machine learning applications.
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Why is it important?
Our paper comprehensively evaluates four AdaGrad algorithms on five diverse data distributions, showcasing the impact of cosine annealing on their performance. By providing insights into algorithm behavior across different data types, the paper aids practitioners in selecting suitable optimization methods for specific tasks. The demonstrated benefits of cosine annealing offer valuable guidance for enhancing algorithm efficiency and effectiveness, advancing the optimization field in machine learning. The study's findings have practical implications and contribute to the broader research on optimization techniques, fostering a more robust and collaborative research environment.
Perspectives
Our inspiration stemmed directly from the theoretical concepts in optimization algorithms and their applications in machine learning. We were fascinated by the potential of AdaGrad algorithms and the promising results reported in the literature. Still, we wanted to go beyond theory and put our learnings to practical use. This research project provided the perfect opportunity to bridge the gap between theory and application. Designing experiments, collecting data, and analyzing the results allowed us to gain hands-on experience and a deeper understanding of the nuances involved in algorithm performance evaluation.
Dr. Sanjay Singh
Manipal Institute of Technology, Manipal
Read the Original
This page is a summary of: Performance Analysis of the AdaGrad Family of Algorithms, January 2023, Springer Science + Business Media,
DOI: 10.1007/978-981-99-4284-8_10.
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