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Add use cases: semiprime factors ratio detection #121

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sashakolpakov opened this issue Oct 31, 2023 · 2 comments · May be fixed by #126
Open

Add use cases: semiprime factors ratio detection #121

sashakolpakov opened this issue Oct 31, 2023 · 2 comments · May be fixed by #126
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audience/technical Issue primarily for technical review and service. kind/documentation Improvements or additions to documentation kind/enhancement New feature or request kind/usability triage/high-priority triage/required

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@sashakolpakov
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sashakolpakov commented Oct 31, 2023 •
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This test is based on Sam Blake's preprint "Integer Factorisation, Fermat & Machine Learning on a Classical Computer", arXiv:2308.12290

Detecting the ratio of semiprime factors may theoretically help improving the classical Lawrence algorithm for semiprime factorization.

I used Cerebros on Sam Blake's data and got ~3% more false negatives. However, I got ~10% better accuracy. Given that we used only 20% of the dataset for training, and 80% for testing, this result looks good (the dataset has 1e6 128-bit primes).
@sashakolpakov sashakolpakov added kind/documentation Improvements or additions to documentation kind/enhancement New feature or request triage/high-priority triage/required kind/usability audience/technical Issue primarily for technical review and service. labels Oct 31, 2023
@sashakolpakov sashakolpakov changed the title Semiprime factors ratio detection Add use cases: semiprime factors ratio detection Oct 31, 2023
@sashakolpakov sashakolpakov linked a pull request Nov 3, 2023 that will close this issue
121 add use cases semiprime factors ratio detection #126
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@david-thrower
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Paired with: Tabular binary classification in the Cerebros UI (We should be able to hyperparam tune this on the UI based system)

@david-thrower
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@sashakolpakov, One thought I have is that I just wonder if it is possible to quantize (or z / t / min-max scale) the series to coerce it to 32 bit precision.

I see a few issues that may affect the performance:

  • By default, I think the weights are 32 bit signed floats. I may need to update the Cerebros API to include a kwarg for the data type of the weights, such that we can coerce the weights to match the datatype of the input data.
  • A time series pattern may be identifiable from a quantized / scaled re-frame of the problem. I am curious if z / t / min-max scaling of the training data will improve the trade off in accuracy vs computational expense.

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121 add use cases semiprime factors ratio detection david-thrower/cerebros-core-algorithm-alpha
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@sashakolpakov @david-thrower