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Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters
In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them?
Analysis of robust classification algorithms for overcoming class-dependant labelling noise: Forward, Importance Reweighting and T-revision. We demonstrate methods for estimating the transition matrix in order to obtain better classifier performance when working with noisy data.
Simple example of how to train a network to learn to weight data to follow a given probability distribution.