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.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods.Journal of the Royal Statistical Society: Series B, 73(2):123–214
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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.