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Keyword: bias-variance trade-off ×Clear all

2 claims from 1 paper

1. Computer Science › Stochastic Gradient Optimization Techniques

   Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models

   Rocks and Mehta · Physical Review Research · 2022

   Unchecked2 claims
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   1. Unchecked“In both models, increasing the number of fit parameters leads to a phase transition where the training error goes to zero and the test error diverges as a result of the variance (while the bias remains finite).”
   2. Unchecked“We also show that in contrast with classical intuition, over-parameterized models can overfit even in the absence of noise and exhibit bias even if the student and teacher models match.”