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Research map / Learning theory and algorithms / Matrix and tensor methods

Approximate Multiplication of Sparse Matrices with Limited Space

AAAI 2021 · Learning theory and algorithms › Matrix and tensor methods · approach: Randomized sketching for matrices · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SCOD instead of CODApproximate matrix multiplication on synthetic datasets · Low-rank and noisy low-rank datasetsruntimeHelped
Use SCOD instead of CODApproximate matrix multiplication on synthetic datasets · Low-rank and noisy low-rank datasetsapproximation errorHelped
Use SCOD instead of CODApproximate matrix multiplication on real datasets · NIPS conference papers and MovieLens 10MruntimeHelped
Use SCOD instead of CODApproximate matrix multiplication on real datasets · NIPS conference papers and MovieLens 10MaccuracyHelped
Use SCOD instead of FD-AMM, SFD-AMM, CS, or RPApproximate matrix multiplication on synthetic datasets · Low-rank and noisy low-rank datasetsaccuracyHelped
Use SCOD instead of CS or HashingApproximate matrix multiplication on synthetic datasets · Low-rank and noisy low-rank datasetsruntimeHurt
Use SCOD instead of CS or HashingApproximate matrix multiplication on synthetic datasets · Low-rank and noisy low-rank datasetsaccuracyHelped
Replace VSI with SI in SCOD (ablation)SCOD · Approximate matrix multiplication experimentsaccuracyNo clear effect
Reduce the number of shrinkage operations (ablation)SCOD compared with COD · Approximation error and projection error across sketch sizesapproximation errorHelped
Use SCOD instead of SFD-AMMApproximate matrix multiplication on real datasets · NIPS conference papers and MovieLens 10MruntimeHelped
Use SCOD instead of SFD-AMMApproximate matrix multiplication on real datasets · NIPS conference papers and MovieLens 10MaccuracyMixed

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