We lifted 6 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| joneswack/dp-rfs | canonical | 6 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| extract_random_patches | Ran | joneswack/dp-rfs/models/bilinear_cnn.py code served (permissive licence) · get_code("81692ad1d55bf076") |
| gaussian_kernel_coefs | Ran | joneswack/dp-rfs/plot_variance_vs_degree.py code served (permissive licence) · get_code("bea87559d618f4fa") |
| makeDefaultTransforms | Ran | joneswack/dp-rfs/run_fine_grained.py code served (permissive licence) · get_code("59f610ce680efe6e") |
| mmd2 | Ran | joneswack/dp-rfs/run_kid.py code served (permissive licence) · get_code("848427a8f7d31ad0") |
| polynomial_kernel | Ran | joneswack/dp-rfs/run_kid.py code served (permissive licence) · get_code("9fd38816b29f5ee2") |
| polynomial_mmd | Ran | joneswack/dp-rfs/run_kid.py code served (permissive licence) · get_code("2bd52c58c78f510e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
A fundamental drawback of kernel-based statistical models is their limited scalability to large data sets, which requires resorting to approximations. In this work, we focus on the popular Gaussian kernel and on techniques to linearize kernel-based models by means of random feature approximations. In particular, we do so by studying a less explored random feature approximation based on Maclaurin expansions and polynomial sketches. We show that such approaches yield poor results when modelling high-frequency data, and we propose a novel localization scheme that improves kernel approximations and downstream performance significantly in this regime. We demonstrate these gains on a number of experiments involving the application of Gaussian process regression to synthetic and real-world data of different data sizes and dimensions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.05667")
get_code_for_paper("2204.05667")
have("2204.05667")
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