Scaling Multinomial Logistic Regression via Hybrid Parallelism

pp. 1460-1470, 2019.

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We present a novel distributed stochastic optimization algorithm DS-MLR to solve multinomial logistic regression problems having large number of examples and classes

Abstract:

We study the problem of scaling Multinomial Logistic Regression (MLR) to datasets with very large number of data points in the presence of large number of classes. At a scale where neither data nor the parameters are able to fit on a single machine, we argue that simultaneous data and model parallelism (Hybrid Parallelism) is inevitable. ...More

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