Mirror flow reaches max-margin solutions in homogeneous neural networks where the mirror map choice controls whether learned features are sparse or dense while convergence can be exponentially slow.
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Introduces the Euler (a,b)-logarithm as a unifying kernel for generalized entropies and applies it to generalized exponentiated gradient, mirror descent, backpropagation, and natural gradient descent in machine learning.
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Implicit Bias of Mirror Flow in Homogeneous Neural Networks: Sparse and Dense Feature Learning
Mirror flow reaches max-margin solutions in homogeneous neural networks where the mirror map choice controls whether learned features are sparse or dense while convergence can be exponentially slow.
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Generalized Euler Logarithm and its Applications in Machine Learning: Natural Gradient, Backpropagation, Generalized EG, Mirror Descent and OLPS
Introduces the Euler (a,b)-logarithm as a unifying kernel for generalized entropies and applies it to generalized exponentiated gradient, mirror descent, backpropagation, and natural gradient descent in machine learning.