Compression of Recurrent Neural Networks using Matrix Factorization

Lucas Maison, Hélion du Mas des Bourboux, Thomas Courtat

Compressing neural networks is a key step when deploying models for real-time or embedded applications. Factorizing the model's matrices using low-rank approximations is a promisin…