The matrix is returned with the same column order as if not filtering of the top-n results has taken place. This means that when you settop_nequal to the number of columns ofByou obtain the same result as normal multiplication, i.e.sp_matmul_topn(A, B, top_n=B.shape[1])is equal...
Code Issues Pull requests Python package to accelerate the sparse matrix multiplication and top-n similarity selection cython scipy cosine-similarity sparse-matrix Updated Mar 31, 2025 C++ OneSparse / OneSparse Star 375 Code Issues Pull requests Discussions Accelerated Sparse Linear Algebra with ...
Unlike standard Bayesian networks, SQHNs are relatively easy to scale and more compatible with the hardware that assumes vector-matrix multiplication as the basic operation (e.g., GPUs and memristors). Unlike common Hopfield nets, SQHNs explicitly utilize quantization and implement a discrete, ...
This translates into the multiplication of local and global minima. On the one hand, this over-parametrization may be advantageous during the numerical optimization; on the other hand, this redundancy may yield too much variability in the final output. Looking for a small number of distributions ...
The Compressed Sparse Row, also called CSR for short, is often used to represent sparse matrices in machine learning given the efficient access and matrix multiplication that it supports. Sparse Matrices in Python SciPy provides tools for creating sparse matrices using multiple data structures, as we...
CUDA-aware MPI.In addition, we identified that when offloading data and computation to GPU the amount of time spent in point-to-point MPI communication needed for halo updates during the key computation of the matrix-vector multiplication was significant. By taking advantage of CUDA-aware MPI cap...
where⊙denotes the Hadamard product (element-wise multiplication of vectors). DSD DSD [27] is a training strategy that can improve the model’s accuracy without affecting other aspects, such as size. As shown in Fig.1, this strategy consists of three main steps, which are described as follow...
sparse_dot_topn provides a fast way to performing a sparse matrix multiplication followed by top-n multiplication result selection.Comparing very large feature vectors and picking the best matches, in practice often results in performing a sparse matrix multiplication followed by selecting the top-n ...
What about "medium-sized" matrix multiplication?A more recent addition are GEMM routines which are parallelized using OpenMP (libxsmm_?gemm_omp). These routines leverage the same specialized kernel routines as the small matrix multiplications, in-memory code generation (JIT), and automatic code/par...
where the first term is the reconstruction error, the second term is the\(L2\)regularization term where\({\varvec{W}}\)is the weight matrix of the whole multi-head DNN,\({R}_{sparse}^{\left(j\right)}\)is the sparsity regularization applied on thejth hidden layer of the encoder, ...