SAMPL is an interdisciplinary machine learning research group exploring problems spanning multiple layers of the system stack including deep learning frameworks, specialized hardware for training and inference, new intermediate representations, differentiable programming, and various applications. We are part of the Paul G. Allen School of Computer Science & Engineering at the University of Washington . Our group is a collaboration between researchers from Sampa , Syslab , MODE , and PLSE .
Compiler for Sparsity in Deep Learning
Checkpointing deep learning models as a dynamic analysis