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The paper introduces ARISE, a framework that enhances compute efficiency in deep learning by 50-200× through the integration of low-rank matrix factorization and Physics-Enhanced Residual Learning (PERL). It demonstrates that ARISE can achieve high accuracy in tasks like MNIST classification while significantly reducing the number of parameters needed, thus making advanced AI capabilities accessible to academic researchers. The findings suggest that structured inductive bias and progressive capacity allocat

Author

abishekr136

Language

EN