Sparse tensors to decrease training time
I've learning about PyTorch sparse tensors : https://pytorch.org/docs/stable/sparse.html

From the docs (https://pytorch.org/docs/stable/sparse.html) : "Torch supports sparse tensors in COO(rdinate) format, which can efficiently store and process tensors for which the majority of elements are zeros."

Is one of the intended use of sparse tensors instead of regular PyTorch tensors to decrease training time ?

Yes but indirectly. 

sparse tensors can reduce the complexity of computations and hence training/inference time. Complexity of matrix multiplication depends on number of elements in matrix whereas complexity of sparse matrix multiplication would depend on the number of non-zero elements which are less (due to sparsity)
