Two-ReLU gradient descent collapses to bisector then specializes
Gradient flow on two-neuron ReLU model first collapses to bisector saddle before escaping to specialize to teacher.
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Gradient flow on two-neuron ReLU model first collapses to bisector saddle before escaping to specialize to teacher.
Analysis shows biased patterns cut dense associative memory capacity from N^(n-1)/ln N to O(N^(n/2)), with a bias-induced crossover.
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