Abstract: For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high ...
Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...
We introduce Grokkit, a theoretical and computational framework that formulates neural network weight spaces as geometric manifolds governed by the Fisher-information metric. Within this formalism, ...
Siddhesh Surve is an accomplished Engineering leader with topics of interest including AI, ML, DS, DE, Cloud compute.
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