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stat.ML2023
AdamL: A fast adaptive gradient method incorporating loss function
Lu Xia, Stefano Massei
Adaptive first-order optimizers are fundamental tools in deep learning, although they may suffer from poor generalization due to the nonuniform gradient scaling. In this work, we p…
stat.ML2023
On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
Lu Xia, Michiel E. Hochstenbach, Stefano Massei
When training neural networks with low-precision computation, rounding errors often cause stagnation or are detrimental to the convergence of the optimizers; in this paper we study…