I am studying some source codes from PytorchGeometric. Actually I am really finding from torch_sparse import SparseTensor in Google, to get how to use SparseTen
For efficient memory utilization, I have used “matrix" package in R to create sparse matrix using code: library(randomForest) library(Matrix) documentTe
For a current project I have to compute the inner product of a lot of vectors with the same matrix (which is quite sparse). The vectors are associated with a tw
I am trying to create a dense neural network where my input is a sparse 3d matrix. When converted to a dense matrix the shape is (2, None, n) (where n is a numb
I am using truncated SVD from scikit-learn package. In the definition of SVD, an original matrix A is approxmated as a product A ≈ UΣV* where