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Found 2 papers, 1 papers with code
LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks
We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regression tasks.
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
In this paper, we propose a novel method to compute the learning rate for training deep neural networks with stochastic gradient descent.
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