Finding a single informative bit in a sea of noise

Detect a single informative bit in a sea of noise, using deep learning
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Aktualisiert 10 Jan 2019

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Generate random matrices of a user-specified size, and in a single location set a pixel to true in half of them, and false in the other half. Quickly train a convolutional neural network to classify the matrices ('class' 1 vs 'class 2'). Then use a deep dream image to find the location of the single informative bit.
This is a very simple but powerful example. "Traditional" machine learning algorithms fail (a long, slow failure). The CNN converges quickly! The binary matrices can be rectangular, or they can be vectors. The data could represent almost anything...a single nucleotide variant in aligned genomes, a fraudulent transaction in a ledger, ....

Zitieren als

Brett Shoelson (2024). Finding a single informative bit in a sea of noise (https://www.mathworks.com/matlabcentral/fileexchange/67667-finding-a-single-informative-bit-in-a-sea-of-noise), MATLAB Central File Exchange. Abgerufen .

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Erstellt mit R2018a
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findingANonRandomBit

findingANonRandomBit/Dependencies

Version Veröffentlicht Versionshinweise
1.0.0.1

Adding a screenshot.

1.0.0.0