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Sean Alexander Ridout

As seen in: Physical Review B
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Articles

Distribution of singular values in large sample cross-covariance matrices

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By Arabind Swain, Sean Alexander Ridout, Ilya Nemenman
| Physical Review B
For two high-dimensional datasets X and Y, with dimensionalities NX and NY of order of the number of samples T, estimates of their cross-covariance will have large fluctuations. These sampling fluctuations can be studied by analyzing the case of uncorrelated X and Y, samples of which comprise large matrices X and Y with Gaussian i.i.d. entries and dimensions T×NX and T×NY, respectively.
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Machine learning that predicts well may not learn the correct physical descriptions of glassy systems

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By Ilya Nemenman, Arabind Swain, Sean Alexander Ridout
| Physical Review B
The complexity of glasses makes it challenging to explain their dynamics. Machine learning (ML) has emerged as a promising pathway for understanding glassy dynamics by linking their structural features to rearrangement dynamics. Support vector machine (SVM) was one of the first methods used to detect such correlations.
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