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Found 3 papers, 2 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.
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.
A Framework for Democratizing AI
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution.
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