Implicit bias in overparameterized models emerges as a geometric correction induced by gradient noise and loss symmetries, enabling inverse design of desired biases like sparsity.
Theory of deep learning IIb: Optimization properties of SGD, 2018
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Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
Implicit bias in overparameterized models emerges as a geometric correction induced by gradient noise and loss symmetries, enabling inverse design of desired biases like sparsity.