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cs.LG2023
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
Erin George, Michael Murray, William Swartworth +1
We study benign overfitting in two-layer ReLU networks trained using gradient descent and hinge loss on noisy data for binary classification. In particular, we consider linearly se…
cs.LG2023
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape
Kedar Karhadkar, Michael Murray, Hanna Tseran +1
We study the loss landscape of both shallow and deep, mildly overparameterized ReLU neural networks on a generic finite input dataset for the squared error loss. We show both by co…