Feb 1, 2022 · Abstract:Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural ...
Oct 31, 2022 · This paper analyses the properties of two flat minima optimizers, Stochastic Weight Averaging (SWA) and Sharpness-Aware Minimization (SAM). The ...
Two popular flat-minima optimization approaches are: 1. Stochastic Weight Averaging (SWA) [48], and 2. Sharpness-Aware Minimization (SAM) [22]. While both ...
Recently, flat-minima optimizers, which seek to find parameters in low-loss neigh- borhoods, have been shown to improve a neural network's generalization ...
This work compares the loss surfaces of the models trained with each method and through broad benchmarking across computer vision, natural language ...
Jan 18, 2018 · Is there anything insightful we can say about why traversing the parameter space of a neural network with SGD works so well?
Aug 7, 2024 · From what I understand, gradient descent / backpropagation makes small changes to weights and biases akin to a ball slowly travelling down a ...
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The bias of stochastic optimizers towards good minima can be explained by the volume disparity between the attraction basins for good and bad minima. Flat ...
A minimum working example for incorporating WASAM in an image classification pipeline implemented in PyTorch. Usage. Simple option: Single closure-based step ...
Jul 11, 2023 · Intuitively, the argument goes like this: flat minima should have much larger associated volumes of low-loss surrounding them than sharp minima.