Search Results for author:
Found 3 papers, 1 papers with code
Intelligent gradient amplification for deep neural networks
Deep learning models offer superior performance compared to other machine learning techniques for a variety of tasks and domains, but pose their own challenges.
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm
However, traditional genetic algorithms with fixed-length chromosomes may not be a good fit for optimizing deep learning hyperparameters, because deep learning models have variable number of hyperparameters depending on the model depth.
Gradient Amplification: An efficient way to train deep neural networks
Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
