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.
Numerical-integration of Cartesian equations of motion of a system with constraints – molecular-dynamics of N-alkanes
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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.