<ul data-eligibleForWebStory="false"><li>Knowledge distillation in machine learning involves transferring knowledge from a large 'teacher' model to a smaller 'student' model.</li><li>One effective method for model compression is the Kronecker decomposition, which involves breaking down a large matrix into the Kronecker product of two smaller matrices.</li><li>The Kronecker decomposition significantly reduces the number of parameters stored, leading to reduced compute and storage requirements.</li><li>By defining a cost function and using techniques like Singular Value Decomposition (SVD), optimal A and B matrices can be obtained for the best least-squares approximation of the original weight matrix W.</li></ul>

Kronecker Decomposition of Weight Matrices using Least Squares Approach

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