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Found 2 papers, 2 papers with code
Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence.
Ranked #78 on
Image Classification
on CIFAR-100
(using extra training data)
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
The loss functions of deep neural networks are complex and their geometric properties are not well understood.
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