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Sharpness-Aware Minimization Leads to Low-Rank Features
Sharpness-aware minimization (SAM) is a recently proposed method that minimizes the sharpness of the training loss of a neural network.
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57d8ebf4c2f050a6485f370d47656a9e-Supplemental-Conference.pdf
In this section, we report the hyperparameters of each base model used in our paper, details in Table 2. The only hyperparameter that is tuned is done per dataset using a 10% validation split. In this Section, we discuss the experimental convergence of our U-DIF algorithm to the global optimum. In order to approximately compute the true global optimum, we use the following numerical scheme. (exact numbers vary by network and are given in Figure 4).
Fixed-Distance Hamiltonian Monte Carlo
Markov chain Monte Carlo (MCMC) is an inference mechanism that approximates a target probability distribution by a sequence of states (a.k.a.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.88)
