<ul><li>The paper discusses the implementation of Anchored Data Augmentation (ADA) for data augmentation in nonlinear regression models.</li><li>The authors propose ADA as a method to generate minibatches of data for training neural networks or any other nonlinear regressor using stochastic gradient descent.</li><li>The ADA algorithm is presented step by step, and it involves repeating the augmentation with different parameter combinations for each minibatch.</li><li>The paper concludes by highlighting the availability of the paper on arXiv under CC0 1.0 DEED license.</li></ul>

How to Implement ADA for Data Augmentation in Nonlinear Regression Models

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