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Found 2 papers, 1 papers with code
Random Conic Pursuit for Semidefinite Programming
We present a novel algorithm, Random Conic Pursuit, that solves semidefinite programs (SDPs) via repeated optimization over randomly selected two-dimensional subcones of the PSD cone.
Sharpness-Aware Minimization for Efficiently Improving Generalization
In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.
Ranked #1 on
Image Classification
on CIFAR-100
(using extra training data)
Fine-Grained Image Classification
Learning with noisy labels
