Summary of Rolling the Dice For Better Deep Learning Performance: a Study Of Randomness Techniques in Deep Neural Networks, by Mohammed Ghaith Altarabichi et al.
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
by Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, Peyman Sheikholharam Mashhadi, Julia Handl
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study delves into the effects of various randomization techniques on Deep Neural Networks (DNNs), examining how they interact to reduce overfitting and enhance generalization. Building upon existing methods like weight noise and dropout, the research proposes new approaches: adding noise to the loss function and random masking of gradient updates. To optimize hyperparameters, Particle Swarm Optimizer (PSO) is employed across MNIST, FASHION-MNIST, CIFAR10, and CIFAR100 datasets, evaluating over 30,000 configurations. The findings highlight data augmentation and weight initialization randomness as key performance contributors. Correlation analysis reveals distinct preferences for different optimizers regarding randomization types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how random techniques affect Deep Neural Networks (DNNs). Randomness helps DNNs generalize better and avoid overfitting, but we don’t fully understand how these techniques work together. The researchers group randomness methods into four categories and suggest new approaches: adding noise to the loss function and randomly updating gradients. They use a special hyperparameter optimization technique called Particle Swarm Optimizer (PSO) to find the best settings for DNNs on different datasets like MNIST, FASHION-MNIST, CIFAR10, and CIFAR100. The results show that data augmentation and random weight initialization are important for performance. This study also shows how different optimizers prefer different types of randomness. |
Keywords
» Artificial intelligence » Data augmentation » Dropout » Generalization » Hyperparameter » Loss function » Optimization » Overfitting
