Optimizing Generalization in Deep Neural Networks through Adaptive Regularization
Keywords:
Adaptive gradient methods, optimization, deep learning, convolutional neural networks, image processingAbstract
Recently, deep learning based techniques have garnered significant interest andpopularity in a variety of fields of research due to their effectiveness in search for an optimalsolution given a finite amount of data. However, the optimization of these networks has become
more challenging as neural networks
References
. Baldassi, C., Borgs, C., Chayes, J., Ingrosso, A., Lucibello, C., Saglietti, L., & Zecchina, R. (2022). Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes. Proceedings of the National Academy of Sciences, 113(48), pp. E7655–E7662.
. Brownlee, J. (2023, January 8). A Gentle Introduction to the Rectified Linear Unit (ReLU). Retrieved July 16, 2021, from Machine Learning Mastery: https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-
learning-neural-networks/
