Johannes Hertrich, Tim Jahn, Michael Quellmalz
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| johertrich/fastsum_qmc_slicing | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| fast_fourier_summation | Ran | johertrich/fastsum_qmc_slicing/python_src/fastsum.py code served (permissive licence) · get_code("5606505d6ccf9b89") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The fast computation of large kernel sums is a challenging task, which arises as a subproblem in any kernel method. We approach the problem by slicing, which relies on random projections to one-dimensional subspaces and fast Fourier summation. We prove bounds for the slicing error and propose a quasi-Monte Carlo (QMC) approach for selecting the projections based on spherical quadrature rules. Numerical examples demonstrate that our QMC-slicing approach significantly outperforms existing methods like (QMC-)random Fourier features, orthogonal Fourier features or non-QMC slicing on standard test datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.01316")
get_code_for_paper("2410.01316")
have("2410.01316")
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