Explore the intriguing concept of how Stochastic Gradient Descent (SGD) and weight decay techniques inadvertently compress neural networks in this insightful 55-minute conference talk by Tomer Galanti from MIT. Delve into the underlying mechanisms that contribute to this hidden compression effect, gaining a deeper understanding of how these widely-used optimization methods impact the efficiency and performance of deep learning models.
SGD and Weight Decay Secretly Compress Your Neural Network
Overview
Syllabus
SGD and Weight Decay Secretly Compress Your Neural Network
Taught by
MITCBMM
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